{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "02_basics_pytorch_colab.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlelarge/dataflowr/blob/master/CEA_EDF_INRIA/02_basics_pytorch_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lg-muDhsN95L",
        "colab_type": "text"
      },
      "source": [
        "# Torch basics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jauGsFPdN95O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtCGHBEBN95U",
        "colab_type": "code",
        "outputId": "c909e5a9-243f-4f9e-acbb-4b611e1e3db9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "torch.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.1.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSUuB2pjN95X",
        "colab_type": "text"
      },
      "source": [
        "Largely inspired from the tutorial [What is PyTorch?](https://pytorch.org/tutorials/beginner/former_torchies/tensor_tutorial.html)\n",
        "\n",
        "Tensors are used to encode the signal to process, but also the internal states and parameters of models.\n",
        "\n",
        "**Manipulating data through this constrained structure allows to use CPUs and GPUs at peak performance.**\n",
        "\n",
        "Construct a 3x5 matrix, uninitialized:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsJz3AHWN95Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.set_default_tensor_type('torch.FloatTensor')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3wpWUvBN95b",
        "colab_type": "code",
        "outputId": "315ceaf8-02e8-434b-d48a-f0eff1073f78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "x = torch.empty(3,5)\n",
        "print(x.type())\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.FloatTensor\n",
            "tensor([[9.0884e-36, 0.0000e+00, 3.3631e-44, 0.0000e+00,        nan],\n",
            "        [0.0000e+00, 1.1578e+27, 1.1362e+30, 7.1547e+22, 4.5828e+30],\n",
            "        [1.2121e+04, 7.1846e+22, 9.2198e-39, 0.0000e+00, 0.0000e+00]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0RoPlM6N95e",
        "colab_type": "text"
      },
      "source": [
        "If you got an error this [stackoverflow link](https://stackoverflow.com/questions/50617917/overflow-when-unpacking-long-pytorch) might be useful..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihlg8FEYN95f",
        "colab_type": "code",
        "outputId": "f6ad5719-968e-43d5-d638-aa23e7fa6b7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "x = torch.randn(3,5)\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.2576,  0.5149, -3.2271,  1.3276,  0.9857],\n",
            "        [-0.4173, -0.9798,  0.7924,  0.7724, -1.8473],\n",
            "        [ 1.4409, -0.4168,  1.9319, -1.0410,  0.5515]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uY0fG7UJN95i",
        "colab_type": "code",
        "outputId": "355c16a2-a6ac-4882-cffd-ba7f4ba2fc40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(x.size())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([3, 5])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7iv8f0DN95k",
        "colab_type": "text"
      },
      "source": [
        "torch.Size is in fact a [tuple](https://docs.python.org/3/tutorial/datastructures.html#tuples-and-sequences), so it supports the same operations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrctQk3pN95l",
        "colab_type": "code",
        "outputId": "5d1d7a20-37a7-4fdc-c334-1ec0320657b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.size()[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnQgBXTaN95n",
        "colab_type": "code",
        "outputId": "b0c130d2-036a-40cd-e514-6e63e98ff213",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.size() == (3,5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nEKcqyiN95s",
        "colab_type": "text"
      },
      "source": [
        "### Bridge to numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Lvfh7Q_N95u",
        "colab_type": "code",
        "outputId": "c4576b95-7513-42f6-c35f-a07359532c9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "y = x.numpy()\n",
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.25757724  0.51491296 -3.2271056   1.3276029   0.98572737]\n",
            " [-0.4172576  -0.97979987  0.79236394  0.7724351  -1.8473241 ]\n",
            " [ 1.440867   -0.41679445  1.9318963  -1.0409817   0.5515357 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26Hpys7SN95y",
        "colab_type": "code",
        "outputId": "6a9dbe37-b0a3-43e0-8b00-c1b131436f27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "a = np.ones(5)\n",
        "b = torch.from_numpy(a)\n",
        "c = torch.from_numpy(a)\n",
        "print(b)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([1., 1., 1., 1., 1.], dtype=torch.float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxTMrFj-N951",
        "colab_type": "code",
        "outputId": "c7c695a7-079e-4ba2-e64d-bcc11fb2717d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "xr = torch.randn(3, 5)\n",
        "print(xr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.1207, -2.7052, -0.0556, -0.2825,  0.6743],\n",
            "        [-0.3610,  1.4348, -1.3852,  0.0836, -0.6184],\n",
            "        [-1.3937, -2.2128,  0.1499, -0.8011, -1.1639]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coIEFlsGN954",
        "colab_type": "code",
        "outputId": "985e4d61-5e94-4232-b36d-5f83d269a8f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "xr + b"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-01499b3dce7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m: expected backend CPU and dtype Double but got backend CPU and dtype Float"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83lV0lI4N957",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# solve this bug!\n",
        "b = b.type('torch.FloatTensor')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64GkkiKsN959",
        "colab_type": "code",
        "outputId": "5e08a55e-e100-4063-9214-b44f7bec664d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "xr+b"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.8793, -1.7052,  0.9444,  0.7175,  1.6743],\n",
              "        [ 0.6390,  2.4348, -0.3852,  1.0836,  0.3816],\n",
              "        [-0.3937, -1.2128,  1.1499,  0.1989, -0.1639]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUsUFuLTN95_",
        "colab_type": "code",
        "outputId": "99bca551-17ce-4248-8db3-bc6e5195360f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "print(x+xr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.3783, -2.1903, -3.2827,  1.0451,  1.6600],\n",
            "        [-0.7782,  0.4550, -0.5928,  0.8561, -2.4657],\n",
            "        [ 0.0472, -2.6296,  2.0818, -1.8421, -0.6123]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sa-p74AN96B",
        "colab_type": "code",
        "outputId": "d3b98694-8d5a-439c-f412-c3edcf1e1dd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "x.add_(xr)\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.3783, -2.1903, -3.2827,  1.0451,  1.6600],\n",
            "        [-0.7782,  0.4550, -0.5928,  0.8561, -2.4657],\n",
            "        [ 0.0472, -2.6296,  2.0818, -1.8421, -0.6123]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iex6LbYPN96D",
        "colab_type": "text"
      },
      "source": [
        "Any operation that mutates a tensor in-place is post-fixed with an ```_```\n",
        "\n",
        "For example: ```x.copy_(y)```, ```x.t_()```, will change ```x```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4an49hBvN96D",
        "colab_type": "code",
        "outputId": "a7647a2d-c76f-4809-e50d-a55f7bce683d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "print(x.t())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.3783, -0.7782,  0.0472],\n",
            "        [-2.1903,  0.4550, -2.6296],\n",
            "        [-3.2827, -0.5928,  2.0818],\n",
            "        [ 1.0451,  0.8561, -1.8421],\n",
            "        [ 1.6600, -2.4657, -0.6123]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJNpmRZXN96F",
        "colab_type": "code",
        "outputId": "7d5a3fab-9083-416f-a1c9-fc680f93d460",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "x.t_()\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.3783, -0.7782,  0.0472],\n",
            "        [-2.1903,  0.4550, -2.6296],\n",
            "        [-3.2827, -0.5928,  2.0818],\n",
            "        [ 1.0451,  0.8561, -1.8421],\n",
            "        [ 1.6600, -2.4657, -0.6123]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-eOLttVN96I",
        "colab_type": "text"
      },
      "source": [
        "Also be careful, changing the torch tensor modify the numpy array and vice-versa..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2h8JJjDN96J",
        "colab_type": "code",
        "outputId": "8a3f8646-95a2-4a7f-c52e-ba6a603bb3e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.37830275 -2.1902733  -3.282722    1.0451214   1.6600351 ]\n",
            " [-0.7782271   0.45498252 -0.5928113   0.8560674  -2.4656842 ]\n",
            " [ 0.04716194 -2.6295514   2.081821   -1.8420552  -0.61232126]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1qg1CfSN96L",
        "colab_type": "code",
        "outputId": "e0959e4c-27ab-4354-f9ab-7e4057dee456",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "np.add(a, 1, out=a)\n",
        "print(b)\n",
        "print(c)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([1., 1., 1., 1., 1.])\n",
            "tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0vlUsCMN96N",
        "colab_type": "code",
        "outputId": "ea2380a0-8d4d-4d58-dc3c-b831b2047979",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "torch.cuda.is_available()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEX4sVH6N96P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device('cpu')\n",
        "# device = torch.device('cuda') # Uncomment this to run on GPU"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAaxuUNSN96S",
        "colab_type": "code",
        "outputId": "c1f1f57a-d0e8-4141-a531-ca2f249e5c3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.device"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uij3KV1JN96V",
        "colab_type": "code",
        "outputId": "69f7a5fa-5c98-4050-ea00-2cbe1cf6c2cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        }
      },
      "source": [
        "# let us run this cell only if CUDA is available\n",
        "# We will use ``torch.device`` objects to move tensors in and out of GPU\n",
        "if torch.cuda.is_available():\n",
        "    device = torch.device(\"cuda\")          # a CUDA device object\n",
        "    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU\n",
        "    x = x.to(device)                       # or just use strings ``.to(\"cuda\")``\n",
        "    z = x + y\n",
        "    print(z)\n",
        "    print(z.to(\"cpu\", torch.double))       # ``.to`` can also change dtype together!"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[ 0.6217,  0.2218,  1.0472],\n",
            "        [-1.1903,  1.4550, -1.6296],\n",
            "        [-2.2827,  0.4072,  3.0818],\n",
            "        [ 2.0451,  1.8561, -0.8421],\n",
            "        [ 2.6600, -1.4657,  0.3877]], device='cuda:0')\n",
            "tensor([[ 0.6217,  0.2218,  1.0472],\n",
            "        [-1.1903,  1.4550, -1.6296],\n",
            "        [-2.2827,  0.4072,  3.0818],\n",
            "        [ 2.0451,  1.8561, -0.8421],\n",
            "        [ 2.6600, -1.4657,  0.3877]], dtype=torch.float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvPuAUmQN96X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device('cpu')\n",
        "x = torch.randn(1)\n",
        "x = x.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52yklyCIN96Z",
        "colab_type": "code",
        "outputId": "869edfce-bb33-43ad-a686-505fb70ee690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "# the following line is only useful if CUDA is available\n",
        "x = x.data\n",
        "print(x)\n",
        "print(x.item())\n",
        "print(x.numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([0.0412])\n",
            "0.04117424786090851\n",
            "[0.04117425]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IlRnOMmN96c",
        "colab_type": "text"
      },
      "source": [
        "# Simple interfaces to standard image data-bases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpyyI4tGN96d",
        "colab_type": "code",
        "outputId": "7473b32c-69e7-474f-ad4b-29b78f2f569f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "import torchvision\n",
        "\n",
        "data_dir = '/content/data/'\n",
        "\n",
        "cifar = torchvision.datasets.CIFAR10(data_dir, train = True, download = True)\n",
        "x = torch.from_numpy(cifar.data).transpose(1, 3).transpose(2, 3).float()\n",
        "x = x / 255\n",
        "print(x.type(), x.size(), x.min().item(), x.max().item())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "torch.FloatTensor torch.Size([50000, 3, 32, 32]) 0.0 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHvJa0y8N96f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Narrows to the first images, converts to float\n",
        "x = x.narrow(0, 0, 48).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upeiJJJeN96h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "22f42d71-0805-4f0b-9678-d40ca3620bb4"
      },
      "source": [
        "import numpy as np\n",
        "# Showing images\n",
        "def show(img):\n",
        "    npimg = img.numpy()\n",
        "    plt.figure(figsize=(20,10))\n",
        "    plt.imshow(np.transpose(npimg, (1,2,0)), interpolation='nearest')\n",
        "    \n",
        "show(torchvision.utils.make_grid(x, nrow = 12))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JSC0QCAQCgUAgEAgEAoFAIPhpIR+HBAKBQCAQCAQCgUAgEAgeYHxoZWVTFMyv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\n+8tRRBkK5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+IReBjzoZZRj5bhM7bXWlQGNT0s6XHq+vXsmH0f1y80pW0400uI65YvolPm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fVM\niiAm30pLfF/GOk8SFraPiHx9IoNiSW/W52xkuu8mScYbkJSsUUW2kCTDS3dMNiOlVDofGrTGlYme\n7zqUmahQknrRGmmxvBe3LRZ03T36ux/RmAldfI7WjHWSctFydiySeerQusZSsiH5nST7GlPFowD7\ng8VZvffptNCHew2S1pMsuCTDRwoXFZLEdUCZ2pK9Qq+NZw1oX9IWqZdDa83SEskhSQa3uiZZsGqg\n+i8vQ4I2Pa0p72trWBN2d3fos5C5FEUiur2Fv3PWwXFgFV8cHZRGsU1wZqvQJj8dSN8UKGOfj/be\n2dDZX+/cobWM+is3jbkRlSRTqIdxbvewpwsijOVcUc9ff0gZqMjXWCJRyk9gD9yizKpr65Cm2DJl\n7Bw+W38XMtyZnP7sBMnAVRM2Om6NOwwuZQVLwOupRfuOrEhjOMsWywZtknd7iS+IMPfWb8PfNfZ0\nP56jTJFeBZIKR0FCYsd6Qly4CAn8nZtvpOWdPmULEx/S7dJ8ocxDw0D/nbMCTU3DL0UkBelLO1sk\nS+l24NODIJHOpZeUdZywjPpo5Bif1uTkDnE0pA+QdC6Vy2PsrAn4bLuCtcSd0G0L6HfCZB92OSv3\n2qYQB9Nkl3MkBf3SpJYNX6csz/9iiPnQpN8wyT7MI/samdPiU2P6ncC/Nc7FWMMuZ/RvxQr9PbLp\nXslch6mNIMM+2RcZfof222TvLGdPstD1KZzCxibkPVmp4ollyMM8G+3tdjlrsP5d+mtf/CKqnceY\n3d3UfvIeZbvc66BBr/zJN9Ly2+9q279HvjUY0HroY6wTqX6WMgU2KNugipPsfpS1lH5ftGkPE4pl\n1mhNHwcOCcG+otFEe+pSzhcxNn4Ba3ajj72kp/RnCvS7d0B905HxKRdh9wsLyKJ47SbGbNBK9t4U\nioSkXsWy3ouUK9hP9ak9DcowXq3pflicxr61XISEuR/gs12xsTJJwn2a809e1qE3CiRxe+cWxneN\nxvqDwDCHDAwMDAwMDAwMDAwMDAwMDB5gmJdDBgYGBgYGBgYGBgYGBgYGBg8wPrSyMmWxZIOpZ6C0\n5YqahjZ/ArSwnA2a21Cip7fqoPxblNngqb/9pbR87S1NJ+4PQRv2X0SGshxlE0nob7U6KNUBUUxV\nQmM+RFbiUlYHS7Ij5GZw/yee+Whanp3Sso3v/NsX0muba6CN3dvHfVs9XfchZVwqMPX5GJWTQ9TF\nJJsYZ4XJknRm0CP6fV8/Nzcis0E/Z0qarmpx9ieiuSrKcpbIyiKixmeoXjZlYlAyVi5ljcpNImOK\nUnr8A2LvjiYQ4//EB6/F9pjPxmOuHXX9aPzZn/+1UkqpyRIok+UpUA+LQoOdKIOCmCWZHVNxM0kG\nlxHZEUkulGRM6IJavZ3BfHKJfjktVG2P5D+PXSbKdB1z453bmjK/tQracFyB3WQk88TkCbSxHeBZ\nvQbJJOV6a0CU/AplVJMsGFdv4juFPtEz65S5YAz6Pcxvln3YInliiaPNWZBkznf6+D5nAuOP1oWK\nG41IZ462tYO57pTcQ2SWZF4s6YzoDwkTm+sdUyaWWM4CODNCFHA2O3pukjWQNGg96rvmlqbf7q4e\nTRtWSqmq0IJncxgn3x1fx2Goy9OzkEmcOnM2LbelDj5lMyF11kjb52e172S5w0ceeSQt37ims43V\nZ+Bf9jqcRQn+fT+j5RkOZUkKyEfVaY154qOa9nt6CfN4k7KUzK7oTJyWhe93iH4fRCRdmZIxy6Mu\nyj/av0xSBimb7K7V1nOZfYZPNPp2G3M9IzTmLGX6ymZhC14iPeJkdiS54bIjctY+0bP7JBsqTWmf\nbXkkS2EpMT1kZlav9dz3Q5KCOCRtmJBMkMMs2tBzca9qQ7cn61KWtB7GvzMgGz0mq1OyrgxJVsBz\nPh6Zs7qOmTKttxZlLpOMK+cWYT/dVczTHco2E1eERk/3qtcgYZqkDGFnTmlJQ26H6O5V7CW27+k5\nXcpivuVc2MdeFzKGJGlTu4P532nBZy8t6ee226Dv372HrFFXr0HeOS32Wp6EZKfXPVpWxrL1sWND\nUjLOmOYoloLoNaRLmUL9IvqxtaH3Ttev0H6N7K7QRz+LIkdlSB7uuZQ5jySKUUvPjW4A+7p9DZmJ\ntjd0n1p59F2O7GqKpCCrW1pmXcxivnkO6rXd2dTPJylhnrKk9YeUqWuMbIzRYf8gsjGWonm0p0vC\nAvASOLKe0h8smZMsnR4EtL5PaNlGlmQWLmeuogxBkazpk1P4TfDoR59Iy9+nDMWZZD9Km8JGF/18\n4SN6H/7UU8j+xm0YDKhPV/Va8vIL2Kf/5Tf/eVr+pb+rf2ucvQRpXKt5zNrpsrSefT5lIB6XuZIz\nFEsmYculsAWcNpI+ay1qSZY7xNzZoTm7Jzb45CRkdl+0Iam5SyEu5ha17X6cbPi2ZMBVSqnnKNNf\nIHJnj2R0nCEqiQSRIfnf0yF8+q/PQqr1cFakUZSVMm6T7Nxh+d1BdGqUTU7kVyFl6WNfY9Nak2QN\nrjfgA6dPnU7LffGTHcoUujgLG93ZxZxtdvU9Nkk+Oj+HZ/3hH39TKaXU7b3N9FoiW1JKqS5lo8wV\ndJ+6OfZ72HtbEeyitq+vz81jzFZOom/393Q/9kl6HdKmMZOhsCTyW3BAYQvGYbJMWdpoTvcGsIWs\nyMNHsiGGLN9Enw7kMxOT2AP1SEqc7He2q1hD725SyBbuO1vXrZSHjcd0L1fkijkKA5OlLLqdAa9h\nup93dvCsTBZ9n6V7dGtB0sj02tlzkDs/fElno1vfupdeu34P+8dOb4xP+BlgmEMGBgYGBgYGBgYG\nBgYGBgYGDzA+tMwhm063ByGdimTwPqvT1m8wAzqFcOiU8Bvf/JpSSqknzuKt2/Y23uzOXf5MWs5N\n6s+89MO/Tq/d2cXbuHwJbwT7Eli3kMdbz0ChvtMSwM6mN6EOvbn36fqJE/rUdPkRMF5mFnF6lZFT\n1xq9Gf6L7e+l5SHRgZp9fSIwdwr3mjuJE3jLp8BaY+Dl8VlP2Dy5DJhHNgVC7nNQbHnDbhPTJpPD\n29bihA5UGVkwNyK0KMunU2L5SNDDKYRLzCGmUMSqLvfFW1fHxb0iCT4ajbAn6DR37GnwYRyOo64d\ndf1obG3ok6ywC/viE1aVMlrQhkoF/VGgN9UTEsS1UMY1i06JMnJCfnYRJ4ef/gUEe99cQzC26q7u\nuyDAmD1ZxjzbyWJ89s/r+hSWMAe6fZSbEv2PXvyrJgVQDJhVl7BbiME3pACplgRFzc9RIMw6BVVu\nHH1SUSjAnht0EpqcCPJJKJ8SpqfPFNDcoSBxvod+mpcToVYbJz+M9Blsy2MYQEoplRHmBvuSfh99\nxwGplQQKDIkpw0GTE5JQQMEaQzrd4OC1CQugXgVjZW8HLIOWnJBVKhTo9BBUd3R9PGJ9ejPw023q\n53pd12dqDsFtP/npC2jPUNsSBw7lE+9ej09r9HNrdErUbuHU6+tf1+vDNJ2EDqi/mIUyEHu1e+h7\n3+fg5ejzhCkXUbD4IZ102QnVyeaIx/h+twXbn8xp9kiZmDSuc3SkZD4YZiacn5wik8k4dHLMp3G5\nZEw4Liid7CbEr4BO2qMBsWPIwQeh9Ck9a0inYoOh/rtlUzBQqqNHa2fCWGi1sY5bNA9HfLpMJGaO\nOcRusCXQtcMskyHsp9/j9eHoc7SU8Rrdx6md1GtqBkGZz50DW7i5oYOXz05izOfq8FV7NWKDSZ9n\ni2iX62Ffsk/7hllJsHHiNBhJlTY+OzOn/Th3oUtByGdmwPzL5fW8H5A/tGh8+8IC2dzE6XZIjo2D\novdkYeAkFdk82j4OvkunuccE+7eIhe6Qnx5IMF03hz6IydZCGfOb1+ADvRBtvHAZ+6xAJpXXhA/N\nEEuNmWGRrG21NnzR7iqYA1akx6FEQVMXp8kWqB/XupqB1QqxV71CCSdyPd32MxQ4vpjBPAv5J8Ax\n1HJm2iZ+1qUxG2XVDA78ncsBs+dlb5PPYV/y0cc+gWoJmzSivrdpT8fP7cu+w6K95uWHH0/Lr70I\nZk9e9rixhTmSJcbzF37l15VSSuXIPoZU7wz1oyW+8cobCH49IPZbdVfvreKLYA7d2cB+ayzIl8T2\n+LUmDU7NLCNigyaMIfZkMTOmeVOWMP9D9GeW5tanPG1Dny7Db32qAobG2l3sW5OkOFEB83SpQ8wv\nWmdDGb+A6tUllvKSdOMX81inf2kBdViewDxxJDJ/HOBaXCVfUsM8GYfGHgU3Fhtzxv1MUO/1O/q5\nWxR0v0C/gaZlz14sYpwmZ/B7q1RG+d1rOhj7a28gCdLnPovfsMlj6zUwotqcDILY8/WWtiFmCBdI\nBVOhoMgJS3x9A/tWNwO7Kwk7tUUsRAe3VQHZ5VDWpegYxq1j0f6BGIAu7Y3CofZnHrFY+8TgHxKD\nL9mzcaKErR1K8CHs9UYL/dWs4/sW+dZsRtQzFNzaz6I/zl/UDNtrN2+hQbSH4R2DLf/rUJB6RYxl\nxyMGpthSjrJMPHQeDODBQLdnYw9tvLmO9WOZWPcfBIY5ZGBgYGBgYGBgYGBgYGBgYPAAw7wcMjAw\nMDAwMDAwMDAwMDAwMHiA8aGVlbU40CHJHYhBrlptoWjFaEbogZr2599+Timl1MY7oGRtkxwqegtB\nERNZWL8Puro/BfrdYBPShE5LU966RIObJfnW3/2NL+i6Zkmi4tC9mvjeglC9uw6ogN0haHD5nKZv\nX7h8Lr32g+++mJb7TaLJZ/UzLj5yKb02N4V6dYdHB8DLFUARdRPJBH1n7fbVtNxogMYWhgmVF/fy\niDIdCeV1ZvE86uqQHCVLQa+F6tu3KEg1S8Woz22lKaAxydlsohOHQnO0ycxdriTHnpbg1dahAamP\nw5iA1PehNFuY1uP/yEMIAlqjoHY9iaZ95ToCQN+6dSUt+z5RHoX+mJ9A3zI1fWVJl4sKNMrBGubD\nf/SlJ9Py//0vvq+UUmp9E/TfiQwooNs27rEf6+d2qL0RyUKDgR7fwoACbVPfDkh+Y4eaepzjMQsw\nN8K+rm9M/T0M0V/tGHY3DgMKmt4nOmpCF+dApxnq22ZD6kA0WYf0O75H8h2R8rCchSnIcZQEXQd1\nlvsrogCZPZGQjdBRydR6JDFL5Dkh9SfLN/M57StsCsrYD2l+k13V97W/C4ka32piHGyRZ0Q2aMmH\nYUv6ubqDAHpVhTpMlCB5CKXtre3V9NqZk/j7/Iz2UbtE3754Ef5ufR3P2NzUNPfZadDR760h8GtO\ngu1X9+HblTU+KGsSfNQiqr9NnGqHkgC0OtovZUg6kyf5jSem3wsxt1yyn6wLfzgh9bm4DNnRgCea\nelG9F9V9UOcLeQ4Cru/rRJh7LDFkD5YElOx18fdqCFtIkwtQgMZyDn7Hp75pSn9ENp7QqGPOTp3S\na5ufZVvigLP4XkfskYOjB0Qx75FUdCAB9vsUaL/XxzxqStEmya5HUkKH5EjHicUSGRPL+KyxQeiV\nStoW09ncmYvwvasyB27deDu9ViA5/fICpMC7Dd3ebp/skvpuQFLB3raev9k87mXRZ72snmch2eoW\nBR8tljC+c/N6TR7SZ+skqdra0vPTI1t2KZC6S7416TOW7LBsYBwGJEtmKV/yBJZT09RUA5K2tsX/\nlih4sVchuZpIEyo25DB33kZA41vvvp6WL17S9zg1B181S/LuIgUcjfq6/4cd9J0f4Xu+nwRgxjWW\nNu3fWEvLfQkiG3HQdQ4+XdL+cmEZ+8cpmmejypijNyyVCvYSgWiUWWrGY5ZIvQJKeMDyL4uTJshe\nwFbY5/XalIQmCUicYQkb6sXjn4RD4Kb0A7I1F22fntNjtlvD3urxx59Ky0XpOw4y7/oYhw75oIkJ\nLfvJUADmfIGkIjJ+TUoGMoyPyxCDenN/sTw36YiYEgdYlDQhyTIRUyB1m/ZAI24puZePfj55AvLu\n/6Sk7bJC0vwk2YxSSvm07/hGTctJ396BdK5GdZguYKwbsqcfhOjnJz2M05endIKHx+YgrSrSpI45\nkLoEhmYDsCZIZpN8b1WNR8xjpuvIyQT+P/beNMayJL0O++729pcv962WEWXwbAAAIABJREFUrOra\nurqqunp6mWH3LGxyNByKI1EYiCIEkTItGzBl2DII0LAEGLINW5ANGP7BH7YIgoRgirLFfRFNckTO\nQk7Pwu7p6em99spasrJyz7dvd/GPiLjn3M6XmT3dINh2xflTUTffjRvrF3FvnPN9LBWNWBo9UGXn\n8X5/FXuRQNu4UgVlafVx/wsfu5ym766oPcq7N/F++uQzcKr++c9+SkREfHJo/PrbV9J0OQe7U66q\ndrx7FzbDp/FTor6MdPt3yGH18m30X6Wm8q1NoG+CAsbKGrkdMObZ9Q92cs9ytz61Xb7A8l/VTiwf\n4/328aOQGBr5/sN1ciXQxLja3lFroEdtNFZGn7gl9ElJb9R8mg+FMklM+2rt9UinH5ELnGoedXd0\nXwXsIoOiFGTeH8bVuFs6hvHeaEBC9sp9NS5W1rEfT+gbyMICB2V6Uz4oLHPIwsLCwsLCwsLCwsLC\nwsLC4hGG/ThkYWFhYWFhYWFhYWFhYWFh8QjjQ8nKHMcZF5FfFpGLogiK/4mIXBWRXxeRE6LIez+Z\nJMnOPlnsXzCKKpF0KAoKBWpxtDf5oIBvXEWK6nLm4lkREXlsErQztwFP7LsuaI5zU4pCWpo6mV4b\nkgf4nQeQXzS3FcUrJM/m9TqoeE0daYtYoTIYgALmRKCbrelIS2GOKfu4b0dL5yLybF4iend9HTR6\nwyDf2QQFLRmi7l50MI31k5+4hPtCRY97+Zt/nl4L+6DE5jiqh27+jGKL0r26avNBBeUeX3wczyKa\ns6/p4F5IdEOSIIVCVD0tTauQN/m5SdDCB5rGmuyAnp20kI6Jmh7Fuv2ZZhvvlQVkoj4kI6RklDzY\nT7/CuI5iUJsAHZnphn0dgeri44jY9PUdjMU+UVsTTXP2GujnbnsrTT+ho/ZNE3X+/ipFs6JITJ9/\nUUkAf+//AV31IVijskYRlQYNTb9tUlkq6DPDHPVd5J84KEM+pmgxWnoyIAlCTEH2/J7uf4qocdSD\nzKLroZBXBHMu/TvJpApEaTW0YKYQ97okQTW0YaI+h0Q3b2WiH6j51yM5ixOiPtFQPcPIwEREIooE\nmBDdvNdVv+l0SC5DUqAGRd9qN5WZbexC8nn6PKKj/MDTan7fX4Y89Oo6qMuDFu4rl9Sc2qXnDmlA\nl2uqzYtzkIqKfFtGYeyM6mvPQ9u2E2pboqPn9JLUJmnUu2++naYTLVF06Fzjy3/6Z8iLFghjg777\nynfSaxMTkDaOj6k5t9pGRCWm7LN0yUSA8jmyFUlcjSxVBDLJXBHU5bkybFyk5RfDhOQfJMnyKGrQ\nPU1N3+lgXIf9g6NOhiQbcIgmb8arG6DtWBbCMsphS9UzcmFvQ5IgJl29xgW4pzwJWXI0hnba7ejI\nhxRhKChBjlDUtG6OshVHHOkJ6bqRm9OxVkISopj6L3bVWBrQjynbdP/Q6WEshjTIOTpbeEjUFSPJ\n4TWBZToeR3rT6Yzkhuzh8ceVxCyk9WeVJGZ37mMPs7mtbBxHdGOpcbY8ykZVKxRldYgG2d1R+bLU\nLE9ypbEq6jBe09JZkrtsbcEW1euqTbsUwY6GongU6c+0A0d/MpFx9kOfohVmZGW6zfeLOjmgNm8O\nVDooYf0YRrivEihb8YmLkHw3p2GXvvTnX0rT33hJRax5h6JdjdM+rUqRbc1WzqHB2KR5GGo5wXQR\ntirXw9y7fxdyleaGWms8kq1V5yBHOHta7YGPLhxPr3ldtg8cye/g14FsBLi9MkpOmzZnqSBHgsxm\nrP4JSErkkxsGM6Udl207njUqIhrPLe5zh2SjfV22Grle+Nizz6bpjpax8BziyJhsd8o62lRtHBLF\nHClqPH2fT+N+geShI0EuEjLRylhilr4nUcTGHknyjJQrZCnZaLm7aeiE7HR+DGN4JtDtz/1Ibicq\nJH30O+q+agN/P0XuIb5JUSELgRq7X5xbSq/9vXnMuZNadh5zvTqQJbs5yMYTPYYd2psl5KJE8ijj\nKIyR3TFjicdwRFGp+7Q/MPcVCiw7wt+vXFN7mPVNrHsvzryYpje3UJ+iloJ3aR2/ex9z/vzjSkb/\n5EW8rx0/iX3YN7/1cppeXVf7hhZJfjlCqU/rxlD3SY8ikPLc2tpS+8vqBPY1jQZsfoveCTwdtWsQ\nkjuEEXBoze9SnzokVzaRMcdofBVpv+S4KKORevYHaLsWqi47DR1VktfIGOWOyaVLTq9HCwvY17BM\nbn1DrQUT9D7V7SKvpZkFlEu7WekO0B45ijQdU3TmvKvKPkV26ep1RETbWlPjeZMk3+NTGFeV0uER\nhN8PPixz6BdE5E+SJHlcRC6LyLsi8s9E5MtJkpwRkS/r/1tYWFhYWFhYWFhYWFhYWFhYfATxgZlD\njuPUROQzIvIfi4gkSTIQkYHjOH9HRF7UP/s/ReRrIvJPv9/82clkhb4S8pfMnnbiGtFJuktfESe0\ns+dmFwyCU5dxghKN4Ut2Xn9N3aHT/oCcy9UWcSLwYFl94Tw2C8dPq3WcPq8+UGyNmTxOQmL64lyr\n0Smxdobpk+NQZhHkc+q3QQEnQ0dPwUncyk2wAEQ7Gr1/F04Tu30wdIIy8hiFf/ATX0jTPc0+aG/C\ngWuDnH32ukiL/vLKpzkenRKUNTPo+Qtn02vP/9CnkO8QdXd1HkNyilbvEsOCTq9a+mvs0Xl8Yb1w\nDs5pB/or7Ve/gm+g33gJ+bKDs0g7h42ZppLwuNLO+shhMZ9OxXTyF49gGYmMJs8tLR1T99AJzvQ0\nMWE0e2Wsii/W4zW01/oOWGIV/SX60nn0uU9f2+OuOkU4cgTt9cqr+CJ98wq+al+8oL6GTxfQj7eu\nkZPwGZyw/OAxxU55bfm19NqdjeU0ffbyBRERqRVQr3VydNcY4MTAm1ZjtEyOI50+nnukoE6XXBw8\nyWcvfTJNr1bAMrny9VvyXkR86kUMjEDPwwaxhZIQfV3VzMIeMY+mJ9COt5aX0/TKipp/W+s4Zc5X\n6PRSH5X2ieUypLE07KBcjS01Dzc20V7rm2D4bO/iVKynncT3+VSuSu2YPCUiIovTKPfuJNm45+D0\n0DAW34jhrNOZwcne/GnlQLEyBWf/V/7Dr8ko5DUTJgkxD+OIHKjGe0/2vZAYgnTasraq2mHhCBiR\nZTopYeZXW9vyNtmtNjGt5uaV/eaTQXa6y6eI5uR2SIwFj9hgPCcH+hTo2i2wsqYev5CmI/28NjEx\nd9uoo+vBFry5/D2V1wOM63JwsB3PBZiz4tIJmXarPGCniXSS7tN2YFs3Y4cCA9RmMG6SHdWmQzqx\ndojtG9EJak+X9/KzP5BeO3UZabegbA3zNEsl5Ntpw3YOEnO6iTXdJ8ZCoUJriWYGFsbAwPAHGAv3\nVlS+6zS3egNmR6A+Hq3fo5Acwix6z69VnsQ8YMfxrt4LnLuI+Zincd0k5odfVHN+bQs2gceS0H6o\n3VHjbZucgVfp9DKXN4708SyPAg+UiaHr6Tq0W7QPoBWvqk9TnQ7mVr2FPqvvoAyi65YQk6bbOjiw\ngEO0bJ+eG+h5mmEQUhvUaf7W9Slvj+z0kNb3Wc20rjcwTzeI0ejnMc+CUP02orl3b4faZoOY46Yv\naX8xLMLenTir2q5Ezo/jPu5vEuPUm9H2mxz05mlfW51WecXE2s2cwFMdmPkzCjzGI9NnmQANxPAO\nTZAScvCbYbSxM1/VvvU6ylhlx70ttcfp9NAGUxNgR/kjnN5ysIlOD2Np/gj28VPawXZQQj/lihgr\nseg9IY2veMhBBPDc2LD8iGk5Ng5GgWEp+T7aqHCIP+qMTIJsRUJ2KS1ahs2DdjR5JD2M+4Rp/fRe\n4WgH/Emf9irEIjL7Fl6PoxaY6RPE/Povi9q5/TwYDbfqsFGhYP6fW1TvM1+cwV4iVyQGqHFuTH2a\nUAAP8WivoPvHIXZMQuwm52DSbWb/YMYwM97ytPaySmWUM3fXp7VE/9sjhsj3Xoez4JUHWINm59Re\n08sh/2u3sA8zQQCKFGwmw24jylpTBxFhViYrInZ2MKeMQ3kaajJWxZwNtKPz7W28c/A8kwTP7Znx\n5hy8bpbI6f4mqW9aDczJ2rhihk2MYyxVxzC3HpKkwTB0eK06fhT3BRvK1gxpb88M/8THcytlVZ9e\nFza/UESbF3JqbtSb2J+0msQ8GmLdcHUgBPJRLdEAfw88ZhSq8u7UMW7rbVIpdNRvC/SOPVvDOp5d\nkz84Pgxz6KSIbIjIv3Yc5zXHcX7ZcZyyiMwlSWK+TjwUkbl9c7CwsLCwsLCwsLCwsLCwsLCw+GvF\nh/k45IvI0yLyr5Ik+ZiItOU9ErJEfc4ceaTmOM5/5jjOdxzH+c6ov1tYWFhYWFhYWFhYWFhYWFhY\n/NXjwzikvi8i95Mk+Uv9/98S9XFozXGchSRJVh3HWRCR9VE3J0nySyLySyIijuPs+YDEF0okG2Ca\no6GbMg3aJyezpTFFtZocB422NAXnUrsCuuBQO2b0yEFWk6RtU0dBRw2qSopz+TJJmN7Ab4faCeP0\nFDtKAy2slAMFzFAEY6Ko+h7qaOiCBZL3nD4PZ21v/+W9NF0pqXy5PSJy5jw+DkrcKHDdZ7Tk4guf\n/5H0Wovo7MurD9J0X1MlXeq1sTLogpfOKjnZT//4j6bXjp+HxGwg+G1J1zMaghq3vgv63YAokV0t\nPfPIWffx42ibjqYLrq+dT6/VidrKzok9LVeMSXLBnpCNk0+Wzg3JYRw7gTUOMplqev3u78ooLCwq\nYt3KCuR7/T76rGwkleT8fIocwu02QM+N9VgKiep5+hRklJvaqfraOp7lkOPntS3QmC9puclUDfTM\nengiTQ8czKOKpjn6RH3sb5O8R9Mz3SIol41t0GTrO6Amny0r6VKOHPw+uH43TYum+i+NYZ7vXoUj\n5IXxg03a9Xfg2HVA8zvWdO9GixwdktClvq3K2KiDhlsiiqhDdsfQ83c3YfqK5OjWODpe28Dft4hC\n2iapWEP/pkmSDCHpwuQMiJld7Sw7IOr6LskON9YVofPsEu65/PFn0vStFfTDg3dU/0yeeCK9lh+H\nlCun6bWuexg3XmTePSUimGMiIr5Pziup7XL6N16C3+6MgbZbLFxXeS5A0psjp8idLmxFPq/leyQf\nbLXIOaXu3iMzoCh//AICEtzowX7faav53dlEG+XIpp+aIQlqTpXhjVsYl+0myT8d1R7DmIIctMjW\nTCDfG5tKTpYvYywOD/bxKD7JO/j0Z5jKdmCr2uRwPCSqtUmubmHcnZ8nZ6tjqv03dtDeJQd96pG8\n67lPK9npmccxltq05Ceacj1G9/TIiWjsoG2rVWX7/Ai2O2Ln1SQxmdCUdabUt5uo740V1ZBr23hW\nRPadHb868V7ZwCiMkheIZKWLoXaW6dH2KyRn7UZyUyIO+twSHI42djEGq7rJj0xiDb31AHZlg+j5\nx46p/QqPj+1NUPJrZTWP5mewT6gUUMZiniT9ep4VaE475PR0J21n1KtcpvFBckYjyfbJlpQyTjXv\nyXthJOUiIkNyaN/V60YrpL0d9d3KLsbzhpbHJCQ1y5E8a00H5bh3FWtGQg52HXZI76nnxuSAu0hS\ngR7ZsyhQz/BJ+jAxD+eloZaurG5CzjBbxRgeP3ICeRXUWOA+naY97tUbSlo98xhkrTNVyCxcsp3O\nIbZ8QNKVgd7jcNAGdvxtZNIZySdJTVlD2td5sVJoh6QrV24qufrsIsrNdUxIzrK7W9fPx7hPYvTv\n3DzWsItPKJn1O++8m1578/XvpulzF5TDX3bgy/J/bq117fh3agpyuOoY7NmulvcE/K5SPNg5MjdS\nQmmHdT86nQwooAV54HW0yw23QlLjPMvhyEO/9hjP8mCH+s84UE/ysGUuyaSSNuyO52s7PYY+OzOB\nPvtnPiTqvrb7LjkGTnool6v3CiZYiYiIlJEvR78xDtYT+jsHrxAKujEKHRrPZrzWarCHbKNazb2O\nvXkOjI1hLMShcZQMu7S+hfZaWYMrkNw11Vdsm2fJFcDyXbV/N0FURLJSwYkprNOzsyq9sY688rTf\nqtXQTnW9Bx30WL6HcRWJlsOTnR/S31nqVdXS1sHg4M3K0889h7LMLKfp62/D5o7reVImCWx9F/O7\nQ++lRjKXIynxgN6dyp4OflBGP3VozxC4WEerJZVHQvsP30Xd/YK6zoGawoDlX7je087Ugzy5RenD\nLo1V0b/9ofrNgwfos9vLcFszpSX3T5yHDNOjfB9ujPzk8n3jAzOHkiR5KCL3HMcxX0g+KyLviMgf\niMjP6Gs/IyK//6FKaGFhYWFhYWFhYWFhYWFhYWHxV4YPFcpeRP6JiPxbx3FyInJLRP6RqA9Ov+E4\nzn8qIndE5Cc/5DMsLCwsLCwsLCwsLCwsLCwsLP6K8KE+DiVJ8j0ReXbEnz77YfIVEaHANhIRjdIn\nOnJO0yP7FI2mUAJVc3JWyQIKpBTyyNt8QlHOilrO5BG3lWVDR09AxrB8QtEja3N41oXLkEmVyiqv\nKkW76fRARx4QjTnSz3OIzhZFoMF124omWyJZWbECKvDiSVA1jy8pyuyD+6CgbWxCJlOahxxpFIIS\n2iYIVAecPIV6/ec/Dao3R0dZ1VIblr4skezjwkklbZqbAd0xClAWR0Drc02fRiSto/5nqV4Uqzbb\npGgzfaLqhZoy26dIUE3yTG88+YuIxKGiAw77RIOksRCksjKUJaIIZcLRavRvcsHeKBrvxbFjR3Re\neNa1a4hAtxsrerVHUsNqGf2fIwlRQ9M937mGKF3FEn47Pa76b0jjfm4a/TAIcb1SUREkzj+Bcdsi\nPcvtbVAed3ZU+oWncP+na6Bvf/XPFFV7tYH2/tEfB819vECRpzR1eGwCFNVb42jbe3fUuPv7XwQt\nWXoUjaZPfTICEUkJ2xRxz6+otimQBKVPtNDNDTXGdncw7l/rQt4xuYAIgu22arOIIpvcXUZUuC0t\nTbpzG9f8Kkk+iQbbaqr5GxH9u0zRDotltFO7pGxEj+ZAjyKP1HfU9VsJyn3lHiio93ZgKAeummel\nWZTL8fBcIydjGvV+ePrCC+p++m2OImoEPkVM0fT5OsklKyXYxqlZRb8dcOQSijA0mZBkQj+Powry\nPOvpsVJ0MK6HFJ2pm8NzDbM4dGmtmaJoJSXIJHMVlUfDw3i/vQ0Zbr6tythuwE77BfSjT5KIfqTG\noOuQLTmkzXMByTuondJWottDkjslJEGOEtW/LEu6s43fnj6q5HfnzmHuTpHEcYciYi2dVL9tkrzP\np6hwuYJK31kBtb7VgKxESP5d1SF+hj3MkQ5F5/J9ihpZUW22u4P58IBkcq9dUfT8rSaFsKFIQCwr\nFefgNjdjjdeHzFrB9HyzbpBNdzkqkJa28ZyvTGKdP3Me8ry3X1HSo/UHGF9n5hGd7dxpyCRbWvLU\n7lB7UUSlgo5W5lO5C3nscSoV2luNq7kR0ho4R3KEnYZaZ9coEtjGNuwpm+l+T42rnkOU/OHBYYUu\nPH4pTW+RBP1dLZle38X6NKBQcDsxS8lVPau0t4qoDoWaasf5RUizZ0gelpAtaeowSAlJzTYpEtAu\nTbqeXmMmFzBfnjh7Jk2vbqq2W74P+feYA1t0ZBJzrr+p+rLsUT85kDNt1lU7JBQ5d+7YsTSdo3E3\nKuoXI+GoXdrmurQfz0Qmc/fmxWMlJpsd6HXFz6Pt3nkTrkirWo54dAFrfoddAlDbGhtWKmFf0+tC\nllwuYz2LYlXG40un0muvvvZKmv7WS98UEZHnf+B5lJXWLY6SfO+usv/zi5AHsjTyvp6f/Q5HpXz/\nsjJGTGNUTCTfNkUjI2mkTKh+d0g+yMh0k5aQ8W8TkpWl62yRIl9RxCyni71ioteVmB7gkZzJp8h1\nsZbURhnbS/ZQz7M4D1vlFJEWimxsljCOUCg12E6huT4KHA3bRCnja6HD7YH5YMY+7zUaDYq4aORu\nwust0h69K6zrd6sCyaiaHfSvq/ePbKc3t7EvXVnFni4akVo8gr1qs8mycjWuWGo4JOlaQb93Vilq\nWLNNUUPJvYeJfFmp4H1tFEozsGWfOAGXINPTuG/56hUREam3MHe6FJCvSRExjXuPnIv6elT3yZpq\n5wlyzTEYoO2nJvDc3W1lN1oc/Yvse6CHwsIM1ttCHr91XczvbleVoU396Hgk0+6jDrdvKdcJTZLv\nFXIYd5WqSueLtAfqoW1C+r7wYfBhHFJbWFhYWFhYWFhYWFhYWFhYWPx/HPbjkIWFhYWFhYWFhYWF\nhYWFhcUjjA/rc+ivDB5FcuhEoBjnfVDpKjVFtfWI7jqMQM9yNL2+0wTdsRwj3wKxI2WoqFgu0V1n\nJ0GZDEugdV14RkmtPGJqPjYBqu7dDSUXqO+Azhrk8eMhRUkKddSVUp5kZSTvqRZ1BLKEo36g4EdO\nzaTp42eUbKvRBqW/QdE12BP/KOSJ6mkYrTmibx49CVrv6UtPpWnDtL19E3Km6hjabqKiy0u05RxR\nUwckITO03X4ff6+U0b+1MdABDU15l9qZJSaBlqg1u2jvuxTJpVXHfQMtN0tCUPmY9olre6nVIiIJ\nR33Q4OhM+6FUVNTRM6cRjWaiBprineVlERHpkQTqZAX9kLjos3euKJnSdh101u987+00ffEJJTGY\nm0b+JRfU1QdER/03//YN9awTqMM/+geg8r+7jHl444Yab5cfR9s88RTy/akXT4iIyCAEzbI6gf79\ni28gGtnGrhqjp47g7z/xIx9L020duc4twCZcfQuU2npnT+DDDMYXMU9370DWNV5TdNIji5BD7pJ0\nLtF25U6MMXHzCtp2mmRhRR1tiGn4LFesltUYDnJoj+NH0aekKpMbLdWXEVG2XY9kpz2iqWrpkUPS\niYTkG9+7oaRxXoBxHzoUZa0I2WfRRC7kCDYJ0q5jZGWHSycnJtV484iiznI0jyjgJhpln+RQAVGu\nS45qu2IR4ytHdouf0dd2tkt2bxii/6Khalvfx/0PBH2yQ+mwoajcA5KiJvMo16ACu9H1dZ95oCAX\niOrviyp7Yx3ztExSsLEiRec0jyNJhnvwEJcm2bVyFTRpY6JcaqNwyHaLohHp6y7Zl9dvwj50tCRj\nqYiIGd+9jQgjRmYhIvK5v6Ft3BlIZ4YUYeiP//BrIiLy2quIFBSQXKVYQLpWVXm1KeokU989D/nm\n8yZiCvrh/jraZrOuxsWQJHtDinzmuRzJRw6EkRPw+rAfzC9Y/hl10b8muppLsoEhpUuzkIqd06Zx\nEH49vbZ672aansxTtKicGle7a5CV5mkeVnR0noikhlskV2h1MBaqVbUvqFSwHldHyF2rFIFuchr1\n3a5jTu5qCeIWSXYbtGcbhRde+IE03ScJ2vO6TRsDliCgPgOKYlbXEQR7JAXJF1GfipbMVGh8OCTf\n6ZG0KSmo3zSGsAO3HkI2Wqdz2Ie6bjWKHjtdQnp5a1lERI5R9J9L85hnl05A6h889xkRESnS+MiV\nYBtjbXemKXrsQhXpAsmkCsWDJTfNXfSPGaNOzJJdErFoY8OyVQZPk6KObHX9BiRdjV3sJZ888aKI\niASC9vB8zE2OIGsiTEUUaXZrB2NpZhpSvkSvbcUK2uMTz7+Qpu/cuavzQr3KFGm4Q7Lzhzoa1PET\nJ/CsGezNpx4oyewGuWNYJLniSLDUNCGpGEVXlJZeJ2dQB7eGMqZRrFgWS3JWh9LpEGU1LZdHt1dC\n+48kT5I+ctnh6HnmxrxfRlYxveMYBRBHMGQraqSCvG5xhLJM3cx6RlGlEq7jIfJglr6ncjJaU4a8\nXlIUxFzO1ZfonYDKaKKc9endj/eHeXIPYa4Xy7CnBXp/rGv7Ua2gvfm5HK3Y0a/4MwuQ1nUpoiuX\nx9W2YkDvQAHtrczS6JEbgCrJkpOAbGtbr7Pd0XJGg9//w6+m6U9/GpFzZxchN3v1FWUXegOU69hj\niBRejrB/vKb35GMF9FmRhk2prP4zPYX5MqSIe8UCyru9qfphyFFcaUPt6X3jHEclJ1c0jTb2Gjn9\n7tsie1gmm7+7CtcodW1nedjyOpvX/V7nSJE0LCcpQuCHgWUOWVhYWFhYWFhYWFhYWFhYWDzC+Mgy\nh/hAut/DF8mQnE9FmhniFejkmT6hedpjlF/Cl7QenRzn+Eu3ZiR50Winek6Ar7xnL+mTO3ZIHOK+\nTqJYHs4AZamN4SvwFjmlGw5Uvi7l5UXs9MrUDc83Dq9FRMo1cjg8p75EHjk2mV7r01fR/Gj/din4\nBD7QTqB7DjlopUOgYQ/lLekv6IFPJ0/ESMhrZ4gBsYVicsbFFCzHnE7Q0RI7SuyTJ8thevqAvAoF\ntHOoHZExSyGkEws+FzGMgzhm54b45Bzr/sn4nd7HYakpe5R51mjkA9VmvoffLh3HV+JjS+p0qT/A\nHBhQ+qmP4Wv6iWOvi4jIK6/hBH9lDQ5Br99WJ5kBnVIUfHI43UAHX7+r8l1tos8+fhvPbeIATJKh\nym9lHeO69y2M4VZTtUeTTlpPBGCe/dDnPoG8dD/cuPZueu2f/4vfTdNFfbL72HmwEOo7NNaCgx3g\neXQyXKATQ+PklxkpQ3L2/Pu/9zvqGjl+a9fR9ss3wX4a6pPMnV0w+AbEboj0GCvRCe6Q+jSicZXP\nq/E8oLaTmI5CyJm7o+dRSPMhDjAf6qH67Rg5eM4XcLLvkL2L02OL0aduqYN0dy9j7r0wJ3B80spO\nHivEhDSOHZktVCQmjT/c66yX7RbbDcP8ywVgADHTKdZDwffQ9hvCjjJRRt9RNt0j58gJOdANPfTP\n0NEONOnEyTjdFBHp65O5gaA9ih6NO2IyGGerbGtcZnONwOY6nBMnQgEc0pMq7lPkGxNrJtantRHZ\n8Z0u/v7KuysiIvJN/a8qF526ElPqckPZtqk2+uaP/uSP0/SbbyiHk0NaLz2HTpwjtJPjKrZHJGgj\noVN1x4Ed7fXU/HOovuwUNxJVt4ScVya03rGxP2TplIeaJZJhqdGa1o5gAAAgAElEQVQp4RSdLva0\nQ1lu74CcwJpABuw4uliG3Vok9uPY8fMiIvIkHTNO0mnuzeuwo/myarMTR8Ce2G2CvWZOTR2X1mly\n5tklG/ZQO/b2yAbyHsWsfew4vkftwUERPD1fpsjh9TQFUnjnLphOBgH1M5/2zk0o28aMiITGR0Dj\nMtYs0CGt00P6rXFI7lBAjMzugeyhcaDLLJMOM5ZoDA6NY3ay+WwfXjyhWMTMFj9BLKPZMpi/Rc0o\nCIhOyONO9B7ZZxIclXFjB2379T/7lhyEzXU4jK/qtWt9HWxCZrqNT6gybm0hf2ZXl8pYdypzyr63\nybFwPo/xXiyO6ftHU1och94PtMqgQ45fGy2M0cVjWEsi3SfMNneJRXrqlOoHZkTx/O4Ra/fY8ROq\n3CXUq9XB2D92Qju9JpuysQ5m8khw4AFyqu6yc+kF/TyaO2zT07GbWTOYwkNpUzZeQ4mRkppkWp8c\ntpHsCNs4U6Y11KH132sj4ICj93xJTAEe+N1Ld3bS58hC9P5AezrRdiVhh/YhW++DLblL86iqne5z\nkIL6FvZ0LqlUzHtJHOO3TDJyYvNOQCxFCn7SamEsDfT60KAALoMC+tc4jN6p4+9l2iOFPZTLOK3e\n2SR1BbOYaR/W03vbMo0lXpfqDVXG1YcYt0XaQldmqM/yKt9+ezRz0OCNb7+WpmeIIRwP8F6ysqzW\n1pMXEftqeh5r4OYuVBXrG+rFZHwJrLwgQB0jzWjLBeS0fwN9eq+B96XAV+3QJuUJO5EOe1rFILhH\naE0v0/tuZ+DoP6P/O7uYD+sb6J/dHVWePH3XOHYCa3ZtWn3PiOi7SIn2B9tb5Aj9Q8AyhywsLCws\nLCwsLCwsLCwsLCweYdiPQxYWFhYWFhYWFhYWFhYWFhaPMD6ysjJJiLpIFHOm3PcHisrn7UOTizQl\nckj03wFRbrshO9NTvy2TEzCm5/rkDC1fVXSxmJ0Qk4Tg6GPKqW2BnGqzTK5YJtqe9ord7YAKFlK5\nfFfRxVyid7rkIG1+EXT1Uknl9dgpUO7WN0ABzAcHfwtMaDh4mj7n5cgxNDnw7hBV09FtNzmNsvgk\nXQl0O7DsoD8kqj/RYBNNMQ+Z+cqOOXlcaCmgQ07Ks+5A1TOYGumSc0lJyDGfHmMsG2Mas5GYOUIO\n5+Rg2ZjjHCZGEHH0ePVJHsT07FjXyA9YpkeObklG9+lPfVpERJ64AAfO1+/AMezL3/6miIhsroOy\nyc5eK1WMy2MnlWzr/l04Svsvfv5P0nSH5AKxbhuXnEAyFbuvO9P1QX38wc+C1jm/AEptq6WosjdI\nDvHKt5fT9DPPKKfYY/Nwnsz9GPiQVIwC+3WcnoGTvoJ23BpT/0Y0Ft5864rOn9qrhLb/yrdfRX2O\nHBUREWcfx329vuo/v4E532gjzU51jYN2ljYMyO4ERBcPaqo+x44+ll6bWoLjvvHJBfU7cuDpkmRT\nyDG0UVfyfHIzTi21vOtQwY3IjnYYz/OhTDRYl+o21A79WCo2MTGx5+9hyHRlloqQREjPX7anLHPp\n+zoPsh/5IdGzyQl4vqj6x3NpTaD2ckmO6sZqHrHEySH6fs5X5SkW8fegQNKVeC+V36We8A6xKwvz\ncIS6tgFZx0xejUGHBjY7a+b1zNj/jKSG0jDlJHfwR8vdvvznyqHkN779Rnptk+j5jp6z7Lw0I9lN\nyImnloCxA86Y7kvovtAt6LKSnReiiGtZgEv7B8el9UW4PAdL+YxjT34WywY2SH5T0c6cT59FEAKP\npc9avhWSjIvXhIJP40pfH1s4kV5bpM2GX8NYuHvnqoiI1OsY17kcS7aVrGCsBlvFks4KScHqWgYR\nU3s7tEca6H4ytk5EpNsG/Z8xPan2DVM0z5mS/4evXNtzz9Urb6XpEjlgNnJUdiwbkFTIJ7mqaXOW\nrfP+MXVuT3YvyPFvWfqiJey0gajQvjUXoG28kh7vNMR5vQy1W4E6OejOkXsAn6SPJhjLu+9gvfza\n176Wpk8+plwgnDkNGXafnKau0f5wh543CkWqe1ev09USyV1IUjHUkosK9WOxgH5iZ7x+Sd23sIT5\n0CJpRL6sxnDokJQ0Rhu0Oyj3gxUlqT16BHldvIRAGkEe4zlxjJwJ2YYk+3DjvW4JeHwYp+wiIo9f\nuKDrxWsRMq7oMeh7vE4c7Lw+oXcKTgs52zXpTE68fqSyZMoryWgMKa1z4XXe5Z/u3SNLwO4heH+g\n5xztNVjem5AkL9YyS2eK5OFkO01UjYSlcSwrzGMMJlo6KyRRdGh/mByyds5MYV/a6Sl71enTmk/V\nLZfYWbN6bovcbUTkosRNzLsq6sDvstyo5aJet6iduy3Ux8jK2RWBQ0F9qmWek6rsHDynR2Xk1jDj\ndZwkrPU65H9QHeKuPgVSKA/xXC/RLg76o22+wdkj2MfXV++n6Y0NrJfhUD2v3oQMa3Mb+4eVB3D8\n7+t9S0z2tE1ldLX7jqvX7qXXWiQ75b3i1KS2Dzxuabrk9bvXbgN1LNC+tTqxkKa328pesrzYpTF+\n/gLcbFx68jl1/xjs5fwC1sa8cStD4yvuo2DFFbSdyKvyQWGZQxYWFhYWFhYWFhYWFhYWFhaPMOzH\nIQsLCwsLCwsLCwsLCwsLC4tHGB9ZWVlEsoGE9R0kG+gab93Ev2Ppg6GDcoSSFkX9yUR10FlUexRF\noQTpQ5kkJIZO3ONIYES5HWpacBQTLZwYhMUqRRtzFA2t1yUv91QuI7nI5UDZc6jbjp88kqYNNblY\nRVkXCvN4MEXEGAWXItOEWgrk59EeRYpslHB9dCQ3puRFPsobarqpRxRTUjPIkHmqOvpRjyIT5Ih+\n53osBVNtw1Rfpiub/q/VEK3OY9lhhn57MMBG5cg4eyOUcTpJDqYNi4gMdTsz25W966cRU+gHLlHj\nA5IulbQ0pVwBLXRqFpHrZicVNfG1vwTVcBhzRBzku3xH0S6vvA1Z2YDq26dITb2hkil4MZUrQ3RW\nnU3dL7/527+P/xBN1UhESyR3O3ZsMU1XKmpODuhZ+QJFBQgposUI9Mmu1Ig+a6K+hDQmWCbzY1/4\ngoiINHYQVeDuHUQom1s4mqaXHlOU9nev30yvtbsUmUZHKIwoGl4Y8XNRt2MnlCygRTTphGjUpSnQ\ncsenFI11ahqRDXyi0Xua6s3z0GEpENHnI90nibDNYGq66tPAPWzmiLj6eWM1SE3Zng1J5mDkOSyz\nYPi6vEzPZvp+SHLXfJ6iumhwvka65tB8GrZI3tOm6Dw11X9BHr8dJ5sfDEmuoiVPIUsMKCJWr6Mo\nxkMXa5FDMimPxoWR7fnUN4M+Ra4bBZJGVKuw2SnVmtZTllGy/S5oKS5HGIpJJmOkB15G7sZ2GNhp\nqLZzSRbikdTHRKhkGj1LAVgS4WopHysYBn2KIEdtnjM/onw9khD2TN1YqswSNF6WnIPP0cxaw3aa\n1/FyBf1gxu5bb0EaVSGZ5cKsmr/FPK/5AEerNH/IkRR1cg57gjbJ1ce0FMihNX1nG/Ys1tK5jU1I\nCTp0f5mipOb0GsU2lGWpPS/R9cL6MzeNddghm1/Wka8abdD7r9/F3BsFlsa0Wnujs2TWS5Kj8HWT\ndsmWsPuAkV3O0ZncvXsRlmmyjIb730hquSwcQSzU/bu9BcmXkZqIiHTbkAWaPczDDUTMGZ+GlNDR\n82xtC/3MkydP4+7iZUgbRoHlFWb+su0dDkgqpG06y4eHJI1i+Va7rSQiLkVRrU7CRUG3r8YFR87L\nk6uAZgN7GNFjmKNd5mkfn5BdSSNHjZJWUR7ZyLkkvaa6m/WSxwRH1DJyZ7YjudzBUtVwB/3skt1y\nc/zapsvGax2XQdeN6+Byfdk1hs4roTo6WeGR+oejpdHei/NKzH6GpXMUOdWZWcJ1sy7xXoLcWaTb\nlS7sQ0LycClSxNV078R2nKTmnYNlTk+cgyS/paNUdSha1cMH2A8n9H5nIkuSElgKOZbO6+hd9F7D\nYylgOaspN9mHkPa1ZgyVSabpk40bUrRrI23nKM9BgPsi+q2Zq2wvOxQxc2D2zrRnnB/H+2VziyJX\nahltQkvVKFTKJIcqUgTbCawVrUSV4f4KorB2hzQnabzPaLsRRrBFBYq+Zp623WC5HGzFgGXQOmpc\njtZh3rcMtMw6R649apPYe5drWIePllUZj57DuB4bgyz15PGTKKO2JTvbsOn9IcatqQOZUFmcxbPO\nnEfb/Mv/41flg8IyhywsLCwsLCwsLCwsLCwsLCweYdiPQxYWFhYWFhYWFhYWFhYWFhaPMD6ysjKH\nOFM+61GIardpoitQtJHqGKQLnv72tbUDClmzDbpZhtanqZKNFuhbTKkfhuDHjdUUlbpH1MaQKIah\njtqUEE0/RxEG8i7yzedMxASKIETyLxNFjfNPZDSNfqBpuxzNzCeJUigHS266RHksF7Tkgqi+fgk0\nuApRfF0tmRg2cD9H0QoKWoJAtHOfdHa9NqiLro7a5XtEXY1ZYkD0SU1d7vXQHmEZ5SoUVV5MnYxo\nXCWkGzDphOQdwtGGRqpnRktqvh9ZmZfbS89mQU1O09zdDB2ev+lmwrqpPFl6F+Dvly9eFBGRYzOI\nZnd/E9KnVoauviIiImeeAA2yQJFJhtROHU2/jCgCReBx1D9Vhg7lzy03TlFqTuuoKnMUSWySpAsV\nXYZCmcYSReFLWO84AhwVqN0BDbapI/hEJO/a3gCNtaep7T5Rn+cXEI3g+MlTafqbL6voTKvriBRV\nKsMuRVrWMxziWX6OqL5kC7abqj4zx55Ir80sIfJMaQJytlxB0YIzdo3Ka6jLHGGI6f0s1TDSM5/m\n/1gVNOmlOUX7PbkA+v+//59lJEqa1u9kQp+M/q0BU6ozc2OE3IyjybAk02TBEjSWG5gIQi7Z5jst\ntMe4i4gYZU0LrhOVu0L2LBejfxNtZxMZTWE3Efkin6WoZE+piiaizoCkd532XhkNo0+yo0IRc8dE\ngOSISkPiwbPdMG3KUT9yJCcwEcJo6c3IykKWZ2gqNnd5THxzEwWHFQi8ViSkNzPRGznSj0djnOVK\n5oksH2btWipLJvlHZnzxbe7B52hG6sU2n+VfxSLGjXluNooeftvtqvWQZe08hgdD4uqbyDUUWdP3\nYKePnES0woKmsS9fv477HZZsmzzQBusbkFFwE5R1hDC2JT5JyQOdr0cSWR5rGxQl696qSie0rrV7\nB+sRuD0zUWM1eBRk5FssqTGysswyPkIaw5GReCOQkZKrf4PMHCJ5KLkgaNYHe8qdKYK2BYU8R0Mj\nGV0DdslMg9o47M/UFKR8pr4xl5VmIl/33L3tyBhQCFkTLTIzL1zeT+toljQfu7w/IGnKnZtKnr1N\nMopjRyE7unFdSeJiGuNjY6jjUfrtrN42ZORuXYpAlolcqMB7ApbOGVvANoHTHI3QyNw4Ylsm6nAq\ncaW2HzFuGY0W9kuZCFS0X3a0C4nMlpDsWWLsBu9FM9EwabybduBrJEF29NrqBJw/R/8lTZWue8KR\nJCmilkOuHtJooWQfnEwoP92OPNbIPYjQe4sJJ5bQ+4dQPzidg9fO2QmMq0pJlWd9E/u4Jy9cTNPr\nW4iSlei046Fv+rS/NNGOeUywLWFplNEzc9uxLTDr1XgVaztLmDt9lCHRNt3LRGTk/RD60kSFbdE7\n8KlTiPo3iLo6fzwrl0M7b21QtEM93rPr8V7cX19BudjVQIhy7aQR09AezW08a2YO+/BKRZUnoPcT\ndu/S1HVjuZxDdpqjZBrZaTggVzZdtM1uS427YxQR+LFzz6bpag3uMNyckj5u7ULeu7WNNbDTpSjq\n+hvF8j28f5Rp772xpaKROfSeMDEBWVmrzTLbDw7LHLKwsLCwsLCwsLCwsLCwsLB4hOG8H3bDX3kh\nHOevvxAWFhYWFhYWFhYWFhYWFhYW///Cq0mSPHvYjyxzyMLCwsLCwsLCwsLCwsLCwuIRhv04ZGFh\nYWFhYWFhYWFhYWFhYfEIw34csrCwsLCwsLCwsLCwsLCwsHiEYT8OWVhYWFhYWFhYWFhYWFhYWDzC\nsB+HLCwsLCwsLCwsLCwsLCwsLB5h+H/dBdgPf/Nvv5Cm424vTeecQZq+cPmciIgEfj69dvzokTTd\n89siItLYbaTXNm7upOnWTh8PHOREROTOvbX00uu3r6Xparmcpieq4yIiMlkbS69dfuJUmg78koiI\nbO2202v1dj1Nb9Y30rSvHiulQiG95iZIyzAQEZF+G20wPjWepuePTKTp5z/3lIiIFM956bXIR3sF\nZSdNf37p5+S9+NVf+x9RBi/WdUFepTzaORfk0nReXy9QHfw8hlbgqDycCM8a9FCu9qCbpru6r8Nw\niPwLeG65XMQzfP0MirgXxXGaHg5VHoMB+jmKUIgkxG9F32fuERHp93FfHKm/53Kot0dtE1B75Eqq\njJVKLb328Rd+Rkbhf/riCfUsem6PylsoqLwmqc8nJibTdKVcRXk81R65TD8EKJejyugHGAc7Cdr2\n5hraMR+pflgcx99LPtrOcdB2jqfawQ3QHq6H57quSjsurjkOfusI9Zl+bqMe4toAf8/p7s8V0N6c\nV5Ig/aP/+H+R9+LS9FSajihIYl+PNxoR4ubRjrXxCV0XfE/n8TE2Blsw1P3n0295bkxMqLyqY+i7\nVots1BbsQ6VSERGRxcXFPddERLbWYa+GHWVveFj7ecyXI8eWRESk08F8u3/nTpqOBe1RramxW6Ey\n9qi+71y9qsrd7qTXrt5YllH4H/63/1ZEsu1l2kBE5OGD1TSd99UYKVdgbx8+fJimm/p5i0ePodxk\nK9p12PepGdPXsEW9ejNN+1Oqz0o0LiNaazqC8T4+pebczirau1xGP5TG0f+7O6oMnoexKNS2Jb2W\n9Ic0nzyMZ3FRXhNN9N69e+m1menpNP0vf/6/l/fi5e98a881EYxdHsOcdhxnT9p1aZ46e+/jexgc\nBXX0b/jve/+auYfyirWdHlVWkey6kSTxnrIkI/LK/j2mvyd70s89i30J47/7b/4r9fwI9/v+aBvn\neXvP5Eb1A69V+/UZ/wb5sz1M9lw/rM94DeT7+bmjEMUoSzwiCm5Ma/OoKLlhCJvP9fpff+FX9vx2\ns7E98rembjFd+/rX/jxNX7p0KU0vLS3tKdeoOh4+lvGbVquVXqtWYRMaDez/dnZ3RUTkyLHjyIDy\nNXnt7MCWffOll/D3CO2UtTF7r/W7ak3Y2dqkwnKS1sCesn3/9T/953vyFBF5+sUzKG6ibBSZS5me\nLaXps+ceExGRW8u302vXv3c/TXdpfTft3+9jXapUYf/NnjAIYKeLVTxr6Tjs4ZOfOC0iIrML2C/1\nOihkYwPr7EtfvykiIhee/Ex6bbKGtuusqbXx6h303f27V9P0IKR9p7YbPvXj3/+xv5mmv/byqyIi\ncncFbcC2olHHuDHIB4U910REYrJRrjj6uaPnptmv8v6j38Heu9tD2/j6t2XaX0Rkt6q6zSfL2BO2\n9FgWEdnYRrqnp1+xhOeOU5+1uyiDMbNFqoIT4BkLSydERKQ2iX4ujWNfsr6GNi14pqyoQ0z7w3ZL\nrf9/+sd/JqPwW3+KdyAz7SOySw7b3oTsju4Hz6f3HpqHYVvZ1CCi95cQ47nfxthf21oXEZEdsiVD\nWosCvUd658q76bVPfvo5lCtCXrUZZYOKtI8fDKk+bIfNM+KMgUAdtE01dVVpYBBh3TBZdAfo53/y\nD/93eS+Gffw9DPeuZSIwjU7GRvIv+D+O/i0NJl7f35OnusPZ8/fs/0YHUze2c79Y65m2Nc/gbc3e\nv2b/EPPbCDBsbomIyO6V30uv1c7/eJrOl7C3Dmp4B/1+YZlDFhYWFhYWFhYWFhYWFhYWFo8wPrLM\nIYnwRdEXYhzg46TsPFBf0NZWcarqdnAyVNQMi0IeX9Im8EFZvBCnKeFAfVF+8jJOHBYeezZNx/Tc\nxrb6olsu4Et4jk5YBz11Ql6r4ORochJf8D72zIU0vfTYgoiI3Lp5Pb1249rdNH3m7Hn1/DxO3RdO\ngzkSTNKJ9An15TWq4Le9Lk72B/3RXyINuk0wnQLDLiEGUIdO46Ic0ubEb0intoUYJ+GJPgEJEuQV\ncl7h3pO7HLE2Ap9O1YkZ4unTdv7yK/z1Wn955dNv/jrNJ4bmU/eo02IRkYE+Te0PkH8uRydZGAoS\n6LonMvpLOCM0p7T8lTmTVmWM6ZQpoXLzF3BP19Olb74BnVI7ifpxKGiPbkRtk6OTnZZ6xhQV5cQk\n/h6HOHEaaDNCBB/p8YmA7pNcAaeBfKruusRY0kynyRr6v0MnXWGixnafTgP5tIaZXaPguPyNnk5j\n9Kmkn6dx6zHTSbcdnSLNzMyk6fn5+TS9uanYJcMByjg7izl7TLMbC/Ss9YfI141xKlatKXs0RcyU\nIEdMuuNLabrTVHapS20/pD65dUfZyTt3YV+abZxOuXTaMlbRp9sLC6jj7FyarumxEhEDcD+YtuUT\nfp57PvVft6tOvXL5vcxEEZFQz0k+RYroJN3cLyLSaiqbXpuYHvl3r6OeWyI7zeVqddA2hbKubzSa\n/cBz0owRZiGUSnsZj4NotD02J4MisKl8ah4cMsa5PRmj2EDfH3PI2XPfYWwSfae+fzSDx8zD98NC\nMuCxxG3D89/8Zj/GirE7o9hEqjyj06Ng1rMMM5VO+D2X2860B67FI04Js2UZ3Tb7XU+f63l70vsx\nj0Yxqb6f5/JYMevWqDHDz+IyHMaIYrBNSGiddvR6F5OdZtbEKKbNfv1vyjPqHpFs3dpttXdi1lVA\n83B9fT1NX72mGOndwWiWssnD5CmSZRH5NJYMY3W/ds7n1Zo7PjGaTcb1jasHj/HTF8B0ynnKnnWI\n8RDFSHc0i/XokaPptbkprJfDDsrQbDR0XWAjy5SOQlWfyUns490cynrkKK73NCti5SHYJK5g33Lr\nNhhna6vKvp97CvajvvsgTR+dV/ftolrS2MELRKNLygPd/PEA47ZPDF2zl2RTFu1j/w2yZo8pB+6e\ny0lmK0v71oFqD9/HDwa0Xoa0B8oXVZuXp2fTa9PzWDufOH9SREQeW0Q/vvW976bpa9duUhHVeJub\nwb6nVkGfMjMol1ft3NwFQ6tJ+4onn/mEiIiM0/6jvot+7HeQFr0v9WhP6XNz5Q9+5Y1i9GlqV0bY\nbhERiZE2ay7PvYT61+zJh8SkaWyhvlEbfTLuqjk9dxJ7ygdNMJbv6/3bvXtgfd+4jnlWqGCu56ZU\n2/qC9vZzsIcRsYgMEyail91hSPNb18ej/YlPaW6nUOcbH7I9YBZcQkyszHDX3WDeX/ZgBAuI506G\nJZTua5w99+z5sW6PTJ9TGcyej5lDmRLGe/ctI2nS773R/IaYuBGvrXfVPPOpn4Icza2DtwTvG5Y5\nZGFhYWFhYWFhYWFhYWFhYfEIw34csrCwsLCwsLCwsLCwsLCwsHiE8ZGVlfkx6JsTY5BkBQloedW8\n4k8dOX8uvTZVgSSj3VVULFJZyNQRyCQWjhPNdaCorSFJCZ4r4rk5B7StnKapxhEodds9cLm2GooO\nduc+HKmKB4rZbhMU42bY0XXAs55/DrKzU+eUo2vXx/3FcTy3R9S1+w+UTK4UolunJkDFHISQmI2C\nT9w2z6SJGhmTHCkhqVepoMo+VgO9t1iCPC/R1MRWHfUekiwpIad3vqZwszzAD5jGCHpkoGU/Ucxy\nBdAkcc9eB68iIhH7YtM09FH0fr4vS3cnSiVTC10jkziYNiwiEmqKb0xtn3GqrcvVJse/eZLc+R7R\nRTV1uMQO42iGO7GpA9q2w9XJYwwOtR7pzvpyeu04yXPKRM/1dJ+MkQyrQw4HH24qiRLLzsKApAA+\n8yBVgdgw5QL6hq15quxwNE/9m88d4jSVOorp10Y2FJAEIXb2yhxYYlAmJ/U8Xic0/X0YgqJcJAeJ\nlQk1NwokjeiQkcqRo+OKtmfj5FhuMGDpJOqwranHaxtb6TVO37mvKPMhUXnHJmmeEsV4846i5bca\nNO48tE1ZU5N7RA/fD4Wi+i1LJ1gKxA62H2gHl8UB7O2AqNimHzgv5hCXSpAQjOt2ZilZu0PS2aLq\ny6hEzisPcU67H9guFDU9fz+7YySiGVtEVP9YUDcz3lgaM0pmxeDnZqViRgq2n5PpvbKy/f4+Ov/R\n6dHteIisbIQTSRFQuVneycjWTaX3t+l7Jbsx2ezYZefUh9ty9fzRY4blrKP6LyNHOKR/RzmJPkwe\nyNhPrmbGzX6yxFFtkJHGZfos2XPPfg62TX24XvuVYdT97BjW1eXZqUOywZIulv8eP04Ood9TFk6/\nH3mfcZq/tQV7u7GOwAJ370HKa36z9fLL6TW2cWbO83M3N1CHmUlIdUZJ8rKyQF//S3Jplm/Q3itO\nDpbyvf06AgeIdsZcKHLgCZZqqDWq1cT6ce4JBItpdWGHJ2bVPrxSpf1DC/vDug4oc/c+2iBfQn3e\nehNuJWJRz5ucwntAHGJPnyOXC2Utv3n9VTjw726hjp9+4XFdGazzHHQjF/G+VLV/a4D68l7B2Kt+\nf7jn2n4Ykl3i/YXLtlWneX3IONjVY6FURXvUSA7vUhnPX7woIiIB7UuK1M7PPK3+Ll3MrSNz2D/U\nimfT9M6mDspArib8DgV4oXcJv6r2NkcX8P7QIWffqw+VfKo2h2Ai4zX0yWQNa74Xm+AnJGElDkQn\nPFhzE1D/DvV+KOO6waN3pIzN1eWlvVVE7yJOQf22WELbehQsZG2Z3gm10/SyC7n78TMIwJGI2s98\n0v94eu31791K0zOL6JO50yrtdkmKWsR+x6X6mH1hRGsgv5t5eh/O6q6Q33Fo/htH5s6I4AuM3/nt\nX8Z/9pNRGll6Zt2jx2Zkv3o9pD5ndwkmFZCTctejtSaj2FT/8Sn/HL2ZmL0Gu8jg93x+p/Ny6jce\nvSMJO82mtksDR9Aa6NE70NzDN0VEZJd8Ruw+wPriUYCVD9iFLfEAACAASURBVAPLHLKwsLCwsLCw\nsLCwsLCwsLB4hGE/DllYWFhYWFhYWFhYWFhYWFg8wvjIysqqVdB/A6J1FajEvb6KRhOTHCJPtK1i\nTlEpd1uget3SlD0RkfFp5FvTYcx2SVZ2tAZ6XmcHtN+1pqIFU/AtaQ1B6yyUlPymXAWFcGMDFOPZ\nE/AsX9WRcgZtojNTZIHbbymaa3cLNNztTVCXd7uoz4MtRcX92X/8U6gX0U03G4jONgoRUQhd7d2+\nVAC1cX7uZJpeXARFeGJC0UJLJM/wXDy3pdtrlSM1EOUyJhpcpGmoUUx/Jzp6JnJI6tQdFOQ4RD+l\n0QJIepNxPC9MxVb5coQRnyiXfuDrco2OshRQdCXjwZ+j1eyHSFOLHaI2Mg3SUMuZkj+k8d4nOVKi\no1j0KNJDLCy/0pELClRWF2PUCdG2sc7r9gNESQlCjMFnnzyTpmd0xBSPylgmOnK1qtp/cwdza3cL\nNNraFCRTUxNqziZd1LHVonAhupnGKqASF4tE1T3EonGkDp+izXk6naHBUveZ9mfZEvcJR5aJTKRF\n4iNvbDdR3kk1xl2io252mPpKUeO0XZiYRxtVyhjPV999N02//tYVldc2+ixxSXJXUm1bIop6qQJ6\n9hzRzbceqv5pUASRKzdAXZ6tqbkeHCL/UIXYe4kp9R61uaHHs+yMKfPtrprr/QEo6hM12KhqkSSX\nqUwGBZgkSUZXyxmbTfSNR3T2MvW1kX30erA1HDXMozllysv2odFAOwaBGje7DTy318dYGp+EfNPk\ncVjEJcZ+EiNDg3Zdlp25I++DXEnot3vlSu9HggSJGt8ve9L7R94imYw2+vvJlZLE2XNbJiLTiHRG\nSpb5O0ULcQ6WgIyqY6Y9R0Rqy7RHJgrO3vs9pq5nIrGo+7gN9utTI33ZV8JmosJQuTISNgr7k4xY\nlximPjz194sahwgyH+ysctS4KZVg18bHsY+rkrzmsOeNit7GyMpCVTusrKyk11ZXIVHa3qboStqW\nDOO994tAZjs+AZkN15DzMtHK2CZkA5+qfBOWSHKEILrO+45RWL2HiEkls9+Zwp6PJcimS0KK3nb3\nFuqY8yGpufr2G+q3Ltb5QEhGpfupXsde96lnsf9YX4Vtreg9d6cLO50LSH6xgzKaNeiufr6ISETy\nvq+K+vuRWbyL+NRP0xStuNlXexvuxygz3rP/ihwu4ztxBnU8RhLIXEDRKnU7t2gN29XSbBGRySkl\nxeJxz5Fk87QHmp5R82RzHeP2qQuX0/SYlsy89fbr6bXhLt4p+rvYd7TW1PVyAXuzahVzsjuAPd3Y\nVuOq1cA8PXYarkJivTY+vIf9x9IxvENNT2D9T4ZqDA1pf9Hp07vEfhGvUtDaqdNsTzM2m+5yRkiu\nCwG5KNCWMHHR534O9xwZwxjbuK7abuUO5ls9wn5nq63eJT/1w5/APZvo02IZ42Nmckw/l9Zjiuic\njXZsyohrfo79UqgfD+kH/IYTRbxO67Y7RCL9f//aL6Rpl/JlyZ6rXRdENKdjkm/5vN7pyIY5em4Q\n0XucjjBXpP4a0meQJq35Ro6Yp/2pR71uJHnlMXqfKuC5fZIweiUtyWM+Du2dXHIP4hp5H7nIWJzC\n3PiPLp0QEZHXb2CtefMb30vT+eDg/eH7hWUOWVhYWFhYWFhYWFhYWFhYWDzCsB+HLCwsLCwsLCws\nLCwsLCwsLB5hfGRlZTFFVBoKaHCpZENE5ucV/f72tevptXBIMokxTV1NyJt9CBrs9jooke2OonLl\ncri/E4OuuN2ClGtlVdF685OgZDYHoLxu37stIlnJT2MXkpqdCBTQ+mvqvlhAfSyRR/uVFeWp34sp\ngkABdLX5M6jbT/3sz4qIyCx5q290Ue6v/v5LchCqY4gGcPyYijxw/Oip9NrMNCK95ZluqKVLCUWS\nSwaob1lHPzh58mJ6zWEpWYaerfJotUEV3t1FHXp9tPNAR0HrDfDbkEPTRfrbJ1P6iTaYkMQo0XTg\nwGNZAEWj0NRFJgJ7Gco+1UdzLXu9oRwGRBvZK5cTAXWd/+6TdNLJRNTaG1EtJGpjQXu8j4doo81N\ntN0WSXU6bdV/99YxVq9egxSsNcRzn7+sonp02+hzYvLKblu1w8Mt5OVytLtJKqOmITcbyMsh8mpB\nR4LKcdQIknwk4cFSPpcoovkiqO2eljHF1PbDTDQR9Qem7zMNv9WiuutILEWKnNgVzN83bym74xQh\nGwh9iq5CdPNkW/VP30Hkw4BszXdf/k6avq0jjI1PQh42NT2LOmoZVKsFCdygg7HQ6xLdWFN5K1OQ\nOG3VQVd3EvXbKYp2dhjyJMNiqWiNolGWc+q5HOmB27mpy14bg20ukuSq2yQ7rOUXpQrKWKE+8XXE\nvBpFDXGJGr3VRZ+GOjpavw9q87iLcrPccKilFBwljeVXJvriflGlWLpmIp+x9C0TqW0EeIyOkjll\nZUeH/ZYjhFA0Em3kNjawho6RdKFYQpu+N8+9+Zrro2VnWbnLwTR1DqiVxHujYI2So7H8I6I5z9JH\nNz74HM3IUTlSEMslM/R9/a+fkTBQ25pli/OnesUccdHkSXIJli44HutY9N9JzsT2zsjrOH9WRrsj\nIshk7ufGD/e2Pa+XGfme/g2P28Olkyx92zuPKhRJkiU1LM804y4rcRNKe3v+PkoOJ4L5zXLYLs3j\nGzdvpumWXifjfc5mZ7S8l6OS8jztdyGNMu2YlQJy9D3dD7xGfoAofCIiLkX4MXubAq0fTZKVzevI\nU1EIm7+2Bhs2NsbSJtV2NbLNm3X81tGS/xLJN24vL6fpwEdf93XU4FoeeU1MIJ2jiXRqTr8LXMC1\nlVWssxPzSoL8+BOX0mtFGksXjiCC1B996Y9EROQvvvENlMvhuafdB+DKATJahSefeTpNT09jb85r\nSaht2OpDlPvMhSfS9NycWv87tBbtbEH+1WliTY+Halw+fhIRjkuCMfzmt/5CRESSNu6foMBIYYL9\nw7iW93F0rmoV/Tfmoc8mopquA/aXO6uIQDe+eELXAWNia+0Byk3vhK7uX1LASoGkc1v07jUKQ5K7\nmfGecfNAEeQ8h/egOsIUr1s8v2Pz/kAR22gsOgXMrdkLSmJ2bw178z/6gy+n6adfPC0iIkEF909M\noT05Cu74mNqXDEjeGVO024hsRaDXK7bTSYj0YKjGGu/d+rTfHtL7TjQ0UZgPW68xLuOI1yWShTlq\nnua6aLseRTvuUaMXuqpseWr7cbJbnparVvP4e0/oPZ1kpQ1dnyBA/jnqJ1c/q09rEc/5sEN1H+h3\nM3qlcBzsS1vkVsbT79HDAPvPxSfxHp7zlMT0xrV30mtvr0Ni5gcH25X3C8scsrCwsLCwsLCwsLCw\nsLCwsHiE8ZFlDu3WwRYJPHwxnBrHCe3S6UUREbl990p6rUHMkvJQnS6HRODY7ZBTKxfpzW2VbjZx\nqv7MT76QptsB8u039Wk+sTLcWZwib3aUI7HVBzhV7XXIUR0+8kmoWRwVcqp7dAGn/ZG+r0+nfYUJ\nfIH/5OeeTdPnLqn7Nts4Rfj6fwBb6Ou/hVONUfihH/iRND05qVhCvstHA/iqGvbQTrH+0ukQr4ad\nnnnaMZufw0lYQn8PBvh6PBiofL0mTicm6WttM0D/rzxU7dvq4MQqorw83WbMtPHIGWtEjLTYU590\nXSdHf8e301CfGLDj6awTSPSPYQ68r1M5fVs45E/K9OeRH4Hp1J3a3NflYd+lOfqK7OkTp+UVOEe/\nuozTmso8sRs0e8XzMS53+8j433/lu2n6L195W91fIae51NdjU2pczh3D12/f434AupoVwaydHJ38\n+J7pU9Qrps/x+zlITctCzkkTN3NMLCLZE+kiMYvMqUazhZOnPDlbzBErxtiQFjFx1snh9EA7pzt2\nCSd8YR6Mxgad5vm6dV55i9iRW3fTdJuckzp6bJfKON0sV8CwCfXAmprGs+gARja2YK+aPTWnYjqV\nCdjZc1GflPmHny+E2m4MifEyOQ7WVInyWJxTJ6StPq69uU3OvrXdcWOyRTHaPqSTsKE+Idtqwc6X\nqLhLc+pk+NQR2NsBOaT+9hs4mXE9feJEjtALATn7ppP9UNsVMtnikK0Y6lPk6WmwsnZ2GvRjtLlh\nCWXO3w45cfbI3mVsxUjmkFB6FHOHTvCILfrWd1Xb/OK/+pX02t/6238rTX/xi19ACbQtcfgEL8PQ\nVNfZNnMd+XTT0eVhNqGbcRJKDIq07KMdLJtkRKfBYTSaORSGB9sV05DJiGuqCMQy0Y43i4L2YAZO\n3zWetMnpJp2qUtwAifS4KpGD347gxDlmFpFhP+3DfjGljylYRMLOWBNimY1wqs11d2LTT5T7PofI\nJo/9HD+PQsb/K53WYnxkXGGnqSge7vltwisQe2OVvfNsP7afCX5x9jScCG/tYA9TqMHm9jRjIByg\nXL0O74GG761WZoyPaqdRzrFFRBwz55mVx/nK6P4bhcRBvkUdcGKDmMfMaNneVqfevH1k8l2jj36o\nlZUdbhIzlW2Bo+3pZAlr9/mzT6bpx2hfcWJJpZ88j7WV2R5vX4FT4zv3lkVE5P5DOGB2q2DdGsbI\n3RXU8YXPwwnwj/7459P0xIxie8wvLKbXJpn8pvNKnPc/xlfv3UnTuQx7gRxS6/E6Rmt+i9izJf1e\nMb8ANtDWg/tpmgOLLC2qdxgvxH76ATHenK4az5PjsDUD2nsz062mx12JnCOPV3FfkZh9Yaz3LaTa\nWH4IZ8y1SfWMgPaMzU284+RytA/X+8OY1pohzYd87uD9SpKZ/5rRSGaAnS7z3jwO9zqvdzOBFDRz\niPq/T7ao1SOWsv5JQAPoHgVweTJUzrojYpjnq8RooTL0dN0HNOwCB+3YD7E3crR6xaFgQAOyS10T\nDGTITpsBj96dhoZhfcj2MMhTew5GM2kN3SZPLNhajDrUaW4Ys+HRIjlOzPBWX7/HEYO0WEB9ujEz\ng/T7VAnXKhU8d7Cq9qCDPO4PiqRyoLVz4OmAJmQQuw3sJXcb2M+aIVqlPfIzpy+k6XZP9cl6m+Z5\nQE6zc2x4Pjgsc8jCwsLCwsLCwsLCwsLCwsLiEYb9OGRhYWFhYWFhYWFhYWFhYWHxCOMjKysbRHA4\nxk6cdndBY1zbVnTQMAA9b3UddMR+R0tFBBSybXJuHHu4r6cdkZ04cT69NjUFh3NXXnklTTu6QA1y\n1lusgvJ2/LiSC3RIolYnWuCQqGsV7cjUIUfJlQDlLWg5wy5RYyePQgZx4tzxNP3O22+JiMjXv34t\nvfbdL7+apsPNg+UIcxOgxCbao3A0JNoh0U0dcqDtxEr6EJLsjKU+QV7T5zzKix10RqAuOkOVLpAW\ncGsbMqiQJCgVLZ+o90kWMCS5gZYrDCM8bKsO+marjf4pa0lUrUbSGdJnGUlFnpy9MYbk8C0Z4WBz\nPxj6dETt5WQcmaqyO8Sj90gqkPNJfpVXefkBXaO8Wj3VT2/fhnxobRdSH28c4znuq2fUSEbTIxnM\nGjlzv7ml8igQjb4yhna8dHRJREQmZzFu2SmeJ+iTjpZ9uj5LAdmRqbpvQI7yMjT6QyQ3+TJRoknK\nZ9rXJ4eiOZqHiaa5NojKGVI/DCLMjfu6HYiFL11ysF+eUQ4u85OPpddcov36XZSrtaNo3X1y/Fsh\nDUrgo4zNRJWBHSW6JF0Jtcxx8diR9NpUgSRmVPd1ba96Q4yPI0dATZ+vqXkQDQ92jiwi4mvn4+yj\nOCF7ViGqt99XbdfeZqe65ERcSwzb5AC86KINWP2T9iVLGEPYmik9Fs4sHU2vbTdgo/LUtj3djjly\nSBpRvj22O4Gqb0DzP45Z/mnkXbi/VKSgCR7Rs3WfsWPgfECyghHYz+44qXSSZWXsVDeg68ZZLzng\npHybTdX+b5L0rloBDfpvfPbFNF0jKbgBy7S2tpR9X1/DGM8RFfzM45A+5AMtZ2RpTEa9NcJ27mcT\njNSD/caSFDgr1TlYImx+y/ewO3uWBYVabtAnvUJE/WBkZSydikli1u9hzrkF1bYhSWAnydl/k5y4\nts24c1GyPNH3czrIQGGAfo7IMMUuyyR025LMwiedxdDde+6YkUOxg209xuJ9HIePAks2suovdV/G\n+TkVhcdzIvGea5mNySEKoExfa0emXGqfZJgXL0MGVZtU8+TuTciDN9exxzl9Uq0LDVpPE+GxuFfi\nuF97pbIxlstwfbkZR2vY6RnY3xnbWijCZrAT+l5HjdFiEW1QqUIWNuxi7+WkjuHZaS7qeHRR7Rv+\n3j/8F+m1Ug171Q45/t7SErQ3N3D/xjqcG9++D0lVrAMKODWs3XNHsJ+uVdXa+O0//lJ6bflbf5mm\np376J9P0j31ByWifufix9Nof/Oq/TtOmOh7Jz3kMDwd7+7S+gzXu7i2UO0/tvL2txsjqCt57Hj9/\nNk1/8vlPiYjIV77yZ+m1kBxKP/V3P5em+00l1apv4x3JJyfT1YLq64xqJcB/ajWsjV5LSfEKOe5/\nuN5gWU+zqdp/gqSXHrlvmD2mJHttWjPWNiGnz4xnzXcY0t5uQPLNXFCWg8DroZlTQ3K67JF8fKSU\nk9cl1qMZ59Ms6aU56WVcWKi8ZubgEuDU+ZNp+v6K6icOeFMbR9t3GlgfNjbVPEtoD1XiOtJ10c/1\nyKAGJBU0bjZil4L+7BMsYqyg2tnzD34HYt/JSaZtkW+QVz8aUF7FLtLz5N5lV/+G3XTA0ohs6vfa\nakD7tRLZmnnscef0PiyishTLsHdvbar37GEXY/y8j7xmIpJ667lxn+Rfry7D5nu0Zp9cUu9LY1X8\n9tzxpTT9zjX1LaIVwG4VKN/EsbIyCwsLCwsLCwsLCwsLCwsLC4sPCftxyMLCwsLCwsLCwsLCwsLC\nwuIRxkdWVnbmCUQNuHsTnukb5In/4YaSPkTkxXydZGPbkY70Qiy4mKirBaI2DjQT7/L5Z9Jr/Ta+\nnfUaeG6sI2p1ydP7DMlvipOKQtZuUmQMot89uI/oCJGWUQ2Z0ks0xmkdEeljT0HuNneBpGSvQX73\nnZdUBKm71yGd8PugweUdjmKzFwnVMdIe6yOKKuZQJDChdow03bjbJdkZS1t0fQKSlTBVs9MFfXag\nn5FQJDGPZDS790huaCitXeS1uUERLzQtsFkHzfLGDVCMOxRBrlBSbTO/CCrn8598CnXQY8wdQZd/\n73VDuXffh6zMSEw4SlLCFHFnb/4s7+BnGBldTFKiVh9t9+b1ZRERuXYXEseoAJrtNkUL8eOCzpNo\n0ERBrU2hnUwUtIDovbUxmg9a8tBtQ+JQJgnTWB7lNTRmlvSxVMhEEPIocg5HIzlMyseRKwKKgpZK\nAVlyQ/X1AzUPx6ms7T7m/517FEFMyxxrRMmNKfJR3kQhKING7dMYL/VBv28YmjTNF5+kjeUp2MmV\nDWVX6nXMY6ZRLz2hbAhHFWk00SfjFMltqG1bTBExJqoor2HH9yMyrvvAjN2YbGBE9RkrkY3SEoEk\ngS0Z0ADo6+iOvQbRggsody5A25jgOcRWlv4A5S1r+18skOxpkyKb+SSp1GVIfIr6QxH5OJqkGbuJ\nQ31GUrCSfm6/F9I9KDf3T16PyyL1uesePMb3i6hk+mE/W/L222+n6XpdyQI+8Ynn02uVCkWb0XOa\nIxS9+ea7aXp5GSE5P/bM4/q5KMt3vgO58y/+4i+JiMjWJuxPkWR2P/fzP5emP/MZJZNIwtGan8Nk\npQ7b6RH066z9cPZJ78UoWc9+ErWhliPGND4ckijmtG1kanxC0kuWChva/sodtH2V9h1zR+ZRnkk1\nrkIeE0SZN0pdN4+ycFTR0EF5TOQpjgrmZtRZe9trv2hkaUy5fcbtXxVc2TsfEprHSbonGx3djdMm\nglSHojdtrmOdbfYpoqqWkJtItSIi589BCnT21GkREfnWS4guG8vBkrv92tb8NiOd2xcH6+jGxmCD\nhlqe4dO47FHEpbyWIJVLWH+qRUhfXIpMlbojCNAenT7EIE9+7qdV/pMn0ms3VxHJK2SJel/l8bXb\n+HubIt8W87Dv5y58UkREPvnDkPwt1rCWnDqqpGtHqpgP/9ev/3aa/s3fQfrpZ1TU4DHaTyWC+hT0\n3OEoq7344DEekVR55R7egQJaCzpdLemn9rx8Ce8wL/3Ft0RE5PXvvpFe+4m/88NpurGFfAd1NV67\nTURnY6HkmJbG+DTRM9EIyX1DMK4lZDmU1avALUFMEQJdX9Wh18Bzpybx26WjSurTpbnp0rpzfxV1\nMFpeluFyZMweRckbBZZ/97WrEZ/W44Bq7GW6b6+t4Gilvt77+Ly+0Druk6zI064zeG195llEq3rp\nG6pPO23YY963tkga39pR9dlq4J1znFxnVEiOZmSOtMURnyKyBmXVlzG5rehSBFo3Y2O0VHif9yWD\nQpmjYdLdbRofOpJvbobGEvVDpUUuV/TWd+ws3k+cMaRvvazkmf5Z7GWqcxShuIZ51GkoG9TcwrVu\nhL3qbFntUSrvQuL4KdrTH5ldSNPjH/tBERF56OG5ayu/m6ZbBawPn3/x4ypf+q5x5Qb2U23dJ5cv\nX0Idqe3CAe9hbssHhWUOWVhYWFhYWFhYWFhYWFhYWDzCsB+HLCwsLCwsLCwsLCwsLCwsLB5hfGRl\nZW++dSNNezFRrn3Qso7rqA4LJyGzunf7T9J0fUvRXJs7oGwFHqhphQ7oZCeOnBIRkcnaVHrt5Ve/\njjJMwtP+pcuKIrbWgIStRfKKQkXR0KYWIPkYJKD6jVfAAVu7r8oWE11trARq2ifPKylIaRzPf/Me\nKIJrb4CKOdhU1MSqD6pfQt7TBz32274XUZ/kXZpeOQz79He0Y0L0SyNB4ygq7IVehuq+nV1Eo7mx\nCgnBnQe4vrWjqdhEYV9cBD1vcwsU4UZH1YejVdxfA5XbMEhNBCxVbpLvUDuvatrugKIZvfACxkrK\nriTqZCbiGsu7RPWlc4j8Q/3IyNUocg1HQdBylpBkSUOS2QyIemwUAhQ4Te5vQmL02jVFTaTpIOEQ\nsqLIx3MrBUXF9Gm+ZKQRRIkvV9WcnK4hWpFDciRDeVx/gIgbVYo6d/EipA9Fz9xDkW9IXmHYpAWK\nZuEcIvlgMO1/agpz3UiqOGLT1vbeaDHM6H+wQnLXOuZWpTKp7qE5klHBaMr9gCj5CUVc8CqY62ef\nU9T3hwWSYd6DbUwoyp0ZmrskD5ycRB0NDfrKFUhQxokGz5FFymV1vd2GTbh142aanplQlNoqRX/b\nH1pmRXPPo3Zm+YyhPM/Qb+92MIaffvKiiIhsbKBvNkkO6+WR78K0asfLF84hr2XQbCfHFKU6R9HB\neluwRXNl0K+Lei7vttG2JaL9Viv4bUtTrTkaXominKVR8GiOJCSTDEga1+8re1QqoJ3jfSQkh2FU\ntDK2W/fuQ3L7G7/+myIi8tWvYA38uz/xxTTtaxlUjubhBkVc+va3vp2mn37micw9IiK3bqEf3nxD\nRdksFmGPd3fRp//u3/1Gmj53VvXlwhxkBzzPDgNLdpPUgLNUKPNrSh98jjZKzpqRGnOUGiPvI9lA\njvKfryr7MSBDvkVREn2SarhanjE7jmvbDzGG+x3YkoKWlQ3J1vAG0NPRyBKKSuXRutQmCVpTj30O\ngkVBY8UsJbxm7Jc2+H5kZfv9fdR1Xh840ldfR1oMyYZyhMpRfcpRSbc20c7G3cE2RXqKSZIXkKS2\nsal+E1D/D3pYl25cv6Kez4GESKNymKyM09+POu8wq9Jt4Rc1LTGLOD5bwBFG1W/zJEsfK2AschSz\nutkXOvj76UVEKDx57jMiItJsob29GHu6DdpXFrRcedhniRr6YXYesrFSUTXwCrmi2NnBfmhCR9f6\n+AufSq/9Gtmif/Mrv5amuxtKWvLpjz+dXhvLUZ/psRAPaI9MtrNHe1iDPG2i222akzQuK1p+67po\nzz/90p/Sb1UeP/wipGTPPAN3CWEbUc52t/S4ZOUTyZ0GRr5HeqpcCfJfP6CIm3psl2YQGfXYuYtp\n+tZVyNz6bfUO45OeKSD5fkW7KBi2sTefnYf9X9+GrKfVUu3o0NwKcjT/W+QaYwRCclFgZPAR2Y8S\nR++iPbD5RUx7c96zh1qKx24gWDbG0zSVZyW4/9RpvAN945tqXDUp0nCD1kt2rVEoq318j95rqsdo\n7aTnOnoPwpFk2xSt2hikmPfmvLHlZVgvDEn/YDnrD/7gc3QPknmK9LV1S83v4gzabmwRtiJH0Wrb\nt9Q+PN+hSG7nEI327gPVDouLj6fX5pcoKjH1WT+vKtQieejGMkUdX1F749NUR1eoT1p4J6h11G9f\nvgHp/g2Kwv2Jj59I04WCaohofTm9dm0NNurkBSUnO45iZ+SQkvCq/ofyQWGZQxYWFhYWFhYWFhYW\nFhYWFhaPMD6yzKHXXsEp5iQ5iTpxbJGuq1Px448tpde+9AdfTdO9ovqi19vCl78+nUhPzx5L01OL\n6gv3N7/zzfSaX8RXwOIpfG0//5lPi4jIZx8DU+L3fvu30vTW5lUREVmcm02v8YlTl05QHe2MOerh\nO12JnJd62unpXXK6duvW9TTNpzVBoL44LjyGr650KC7dAX0LxAfMFOEAp1f9UJW3P8CX414LXy/5\nJNQ4xeSv5l06udnVDge/8caV9NpLb91CXjmcGLf0KUJIzr7dN8B0KOZRt4kJ1f5Hj+J0YjrB59Tb\nd5Xz6mYHZeGv+R12TltS+c4vYEysPsCJxPPPf0xEsuynThenTPzVO9DOKd0RDk/fCzMUXHLmN+gT\nG0Qf/fXI+XGnS0wo+opfdFQdhnSKdJcc7O721G8H9E2YHQOP1+C4LYnVuCzRCV8UId1N2NG5OkUc\n0ikDM1p2dTM262C5kY9H6ZxA/xf1SXWOj69orBnGEDt2zDoRPfj8M0/OHEt06jU7O7vn/k4Xpy1b\nO+o0Zm0Tc4Ad6ObJqbHvqbZpk4PFiBxDpzQ0YrmEeocFWgAAIABJREFU9OU/XwHLRHRfVo+D/bL1\nEKd9LZ6T2oEynyxHxAK4c1sxjnpdXMuNE7OI2vSzP6ROa69dxdx76w20R16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1Omy0XYF/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gvWp0gn9pTdqPizZYF7euP5elVx3s4/wZeW2fTqnJDIu+YgGMAoz7XBHrXpUY3jPKGfOJ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\n1CMBvQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P111OkZ9N96j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "d271e2b2-6c2f-4000-b5c5-ec2da93daeaf"
      },
      "source": [
        "# Kills the green and blue channels\n",
        "x.narrow(1, 1, 2).fill_(0)\n",
        "show(torchvision.utils.make_grid(x, nrow = 12))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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5Xlb5zHXUCcpGc4C09C77kuasyyRYte4CoL0G7QfXHcmWGfu4PtDm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eZ85T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+hX3gWGptZb8oJ8di5zg65kfyYljJnPfSWs/+uv8/Zg46wv029e72zXHmWJdckhGOE8dXnhBs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\nxEG0s1QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkulyN3iN96l",
        "colab_type": "text"
      },
      "source": [
        "# Autograd: automatic differentiation\n",
        "\n",
        "When executing tensor operations, PyTorch can automatically construct on-the-fly the graph of operations to compute the gradient of any quantity with respect to any tensor involved."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Ii0jr0ON96l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5e6b1876-8a05-4966-fb12-f1b8c014b3cf"
      },
      "source": [
        "x = torch.ones(2, 2)\n",
        "print(x)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 1.],\n",
            "        [1., 1.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S129Yv_aN96n",
        "colab_type": "text"
      },
      "source": [
        "A Tensor has a Boolean field *requires_grad*, set to False by default, which states if PyTorch should build the graph of operations so that gradients wrt to it can be computed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-47awRjN96o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "86705b50-ab8e-4d2e-fa4e-c0cdacc10f29"
      },
      "source": [
        "x.requires_grad"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwHYlrYzN96p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d7eddf0d-fd20-4d2f-fb3c-9f05b32456d3"
      },
      "source": [
        "x.numpy()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1.],\n",
              "       [1., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5E-TPE3N96s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1cc83c24-387b-40fa-c04b-2de11e89d52b"
      },
      "source": [
        "x.requires_grad_(True)\n",
        "x.requires_grad"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puXW-ZeNN96u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "20735674-0fae-4043-8899-2d23596f84b2"
      },
      "source": [
        "x.detach().numpy()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1.],\n",
              "       [1., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLUOfMmUN96x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "2830a4f4-d821-42f5-d0da-b75f9f0d6c0c"
      },
      "source": [
        "x.data.numpy()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1.],\n",
              "       [1., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cND_NFhBN96z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d31e55e7-313e-41c5-a47a-15016cbc2b11"
      },
      "source": [
        "x.requires_grad"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TA6ZskSN961",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1d638612-a1ca-4d69-b13e-aa610aef0c0f"
      },
      "source": [
        "y = x + 2\n",
        "print(y)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[3., 3.],\n",
            "        [3., 3.]], grad_fn=<AddBackward0>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2W-ZQZoN963",
        "colab_type": "text"
      },
      "source": [
        "[Broadcasting](https://docs.scipy.org/doc/numpy-1.13.0/user/basics.broadcasting.html) again!\n",
        "\n",
        "Broadcasting automagically expands dimensions by replicating coefficients, when it is necessary to perform operations.\n",
        "\n",
        "1. If one of the tensors has fewer dimensions than the other, it is reshaped by adding as many dimensions of size 1 as necessary in the front; then\n",
        "2. for every mismatch, if one of the two tensor is of size one, it is expanded along this axis by replicating  coefficients.\n",
        "\n",
        "If there is a tensor size mismatch for one of the dimension and neither of them is one, the operation fails."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtDyJKovN963",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "71ec2a4a-066c-46f3-a35d-b14f0dcb0ef9"
      },
      "source": [
        "A = torch.tensor([[1.], [2.], [3.], [4.]])\n",
        "print(A.size())\n",
        "B = torch.tensor([[5., -5., 5., -5., 5.]])\n",
        "print(B.size())\n",
        "C = A + B"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([4, 1])\n",
            "torch.Size([1, 5])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szhx4jenN965",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "3f4799ba-c52d-44ea-ab08-54bf1514c06a"
      },
      "source": [
        "C"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 6., -4.,  6., -4.,  6.],\n",
              "        [ 7., -3.,  7., -3.,  7.],\n",
              "        [ 8., -2.,  8., -2.,  8.],\n",
              "        [ 9., -1.,  9., -1.,  9.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGwK8VQ4N967",
        "colab_type": "text"
      },
      "source": [
        "Back to Autograd!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8bx8DuJN969",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9a4d9977-f196-4f66-ef49-2337f7233c1b"
      },
      "source": [
        "y.requires_grad"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DetDXRA_N97A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "191a26ae-e756-4ea9-e396-fb40f7ed907f"
      },
      "source": [
        "z = y * y * 3\n",
        "out = z.mean()\n",
        "\n",
        "print(z, out)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[27., 27.],\n",
            "        [27., 27.]], grad_fn=<MulBackward0>) tensor(27., grad_fn=<MeanBackward0>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AeWcrL_4N97C",
        "colab_type": "text"
      },
      "source": [
        "After the computation is finished, i.e. _forward pass_, you can call ```.backward()``` and have all the gradients computed automatically."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gr2-XzNfN97D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "out.backward()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "El-Aky85N97F",
        "colab_type": "text"
      },
      "source": [
        "The gradients w.r.t. this variable is accumulated into ```.grad```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZS_XJNjN97G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "df336d33-f820-41d5-f01f-0e7f838392c9"
      },
      "source": [
        "print(x.grad)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[4.5000, 4.5000],\n",
            "        [4.5000, 4.5000]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5sbnQYZN97K",
        "colab_type": "text"
      },
      "source": [
        "Let’s call the ``out``\n",
        "*Variable* “$o$”.\n",
        "We have that:\n",
        "\n",
        "$y_i = x_i+2$\n",
        "\n",
        "$z_i = 3 y_i^2$ \n",
        "\n",
        "$o = \\frac{1}{4}\\sum_i z_i$ \n",
        "\n",
        "**Forward pass:**\n",
        "\n",
        "$y_i\\bigr\\rvert_{x_i=1} = 3$\n",
        "\n",
        "$z_i\\bigr\\rvert_{y_i=3} = 27$\n",
        "\n",
        "$o\\bigr\\rvert_{z_i=27} = 27$.\n",
        "\n",
        "Taking partial derivatives give:\n",
        "\n",
        "$\\frac{\\partial o}{\\partial z_i} = \\frac{1}{4}$\n",
        "\n",
        "$\\frac{\\partial z_i}{\\partial y_i} = 6 y_i$\n",
        "\n",
        "$\\frac{\\partial y_i}{\\partial x_i} =1$\n",
        "\n",
        "\n",
        "hence by the **chain-rule:**\n",
        "\n",
        "$\\frac{\\partial o}{\\partial x_i}\\bigr\\rvert_{x_i=1} = \\frac{\\partial o}{\\partial z_i}\\bigr\\rvert_{z_i=27}\\frac{\\partial z_i}{\\partial y_i}\\bigr\\rvert_{y_i=3}\\frac{\\partial y_i}{\\partial x_i}\\bigr\\rvert_{x_i=1} = \\frac{1}{4} * 18 * 1 = 4.5$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dntoWv1fN97K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7c2df40b-2f2b-44b0-e26e-28a191bd12e7"
      },
      "source": [
        "print(y.grad)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSUyH1JBN97M",
        "colab_type": "text"
      },
      "source": [
        "[Why cant I see .grad of an intermediate variable?](https://discuss.pytorch.org/t/why-cant-i-see-grad-of-an-intermediate-variable/94)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgFWrZfxN97N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "e2ce66d8-07cb-42ca-850d-059d5f0b8bcf"
      },
      "source": [
        "out.backward(torch.Tensor([2.0]))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-05c9009a1de9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    105\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \"\"\"\n\u001b[0;32m--> 107\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     91\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     92\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z67yQsRAN97O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = torch.ones(2, 2)\n",
        "x.requires_grad_(True)\n",
        "y = x+2\n",
        "z = 3 * y ** 2 \n",
        "out = z.mean()\n",
        "\n",
        "out.backward(retain_graph=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5StDbION97Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "063dc2aa-e033-4c4b-dc47-34064a493361"
      },
      "source": [
        "print(x.grad)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[4.5000, 4.5000],\n",
            "        [4.5000, 4.5000]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rH9xoB2PN97S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "553096af-7c12-4daf-be29-e1eb73fb39ca"
      },
      "source": [
        "torch.autograd.grad(out, z, retain_graph=True)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[0.2500, 0.2500],\n",
              "         [0.2500, 0.2500]]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_H2Ha8uN97U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "edad1304-3680-4eb7-c00d-9de0f1963ae8"
      },
      "source": [
        "torch.autograd.grad(out, y, retain_graph=True)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[4.5000, 4.5000],\n",
              "         [4.5000, 4.5000]]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQP38Zr5N97W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f831716b-691d-481c-c9cd-529ab3e8cd86"
      },
      "source": [
        "print(x.grad)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[4.5000, 4.5000],\n",
            "        [4.5000, 4.5000]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOsitO7oN97X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "out.backward(retain_graph=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coK7fILzN97a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "23fc2d9b-61b8-44f7-b7cc-fd046b98f42b"
      },
      "source": [
        "print(x.grad)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[9., 9.],\n",
            "        [9., 9.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTFMWvYhN97c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "out.backward(torch.Tensor([2.0]), retain_graph=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KD84EUBEN97d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "a5a957b9-d711-45e1-ae32-7446bb0ce673"
      },
      "source": [
        "print(x.grad)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[18., 18.],\n",
            "        [18., 18.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aro8SojQN97f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "0e23edb4-581f-484b-885d-30a502bd1a18"
      },
      "source": [
        "# Manually zero the gradients after updating weights\n",
        "x.grad.data.zero_()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 0.],\n",
              "        [0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3Oee9G0N97h",
        "colab_type": "text"
      },
      "source": [
        "The gradients must be set to zero manually. Otherwise they will cumulate across several _.backward()_ calls. \n",
        "This accumulating behavior is desirable in particular to compute the gradient of a loss summed over several “mini-batches,” or the gradient of a sum of losses.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmuO-jDhN97h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "out.backward()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEUGKNdsN97l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e1604b51-7ca5-4172-a8a8-1c67a8ed642f"
      },
      "source": [
        "print(x.grad)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[4.5000, 4.5000],\n",
            "        [4.5000, 4.5000]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBi43e5nN97m",
        "colab_type": "text"
      },
      "source": [
        "If you want to come back to the difference between detach and data see [Differences between .data and .detach](https://github.com/pytorch/pytorch/issues/6990)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "5amxt8S7N97n",
        "colab_type": "text"
      },
      "source": [
        "# Playing with pytorch: linear regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DmIIWmoLN97n",
        "colab_type": "text"
      },
      "source": [
        "## Warm-up: Linear regression with numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSFy_HrQN97o",
        "colab_type": "text"
      },
      "source": [
        "Our model is:\n",
        "$$\n",
        "y_t = 2x^1_t-3x^2_t+1, \\quad t\\in\\{1,\\dots,30\\}\n",
        "$$\n",
        "\n",
        "Our task is given the 'observations' $(x_t,y_t)_{t\\in\\{1,\\dots,30\\}}$ to recover the weights $w^1=2, w^2=-3$ and the bias $b = 1$.\n",
        "\n",
        "In order to do so, we will solve the following optimization problem:\n",
        "$$\n",
        "\\underset{w^1,w^2,b}{\\operatorname{argmin}} \\sum_{t=1}^{30} \\left(w^1x^1_t+w^2x^2_t+b-y_t\\right)^2\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_XlKT_yN97o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from numpy.random import random\n",
        "# generate random input data\n",
        "x = random((30,2))\n",
        "\n",
        "# generate labels corresponding to input data x\n",
        "y = np.dot(x, [2., -3.]) + 1.\n",
        "w_source = np.array([2., -3.])\n",
        "b_source  = np.array([1.])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UC6FUZ4wN97q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "7a245d40-975e-4859-e1cc-bdb33144ced7"
      },
      "source": [
        "x[:5]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.93843214, 0.31040763],\n",
              "       [0.33291059, 0.00901682],\n",
              "       [0.58195055, 0.51749007],\n",
              "       [0.35969583, 0.8759376 ],\n",
              "       [0.58456136, 0.38372216]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBM-3fCVN97r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "def plot_figs(fig_num, elev, azim, x, y, weights, bias):\n",
        "    fig = plt.figure(fig_num, figsize=(4, 3))\n",
        "    plt.clf()\n",
        "    ax = Axes3D(fig, elev=elev, azim=azim)\n",
        "    ax.scatter(x[:, 0], x[:, 1], y)\n",
        "    ax.plot_surface(np.array([[0, 0], [1, 1]]),\n",
        "                    np.array([[0, 1], [0, 1]]),\n",
        "                    (np.dot(np.array([[0, 0, 1, 1],\n",
        "                                          [0, 1, 0, 1]]).T, weights) + bias).reshape((2, 2)),\n",
        "                    alpha=.5)\n",
        "    ax.set_xlabel('x_1')\n",
        "    ax.set_ylabel('x_2')\n",
        "    ax.set_zlabel('y')\n",
        "    \n",
        "def plot_views(x, y, w, b):\n",
        "    #Generate the different figures from different views\n",
        "    elev = 43.5\n",
        "    azim = -110\n",
        "    plot_figs(1, elev, azim, x, y, w, b[0])\n",
        "\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYYrvjZhN97u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "3c942b5e-3655-4063-815c-b18d62cc04e3"
      },
      "source": [
        "plot_views(x, y, w_source, b_source)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ldhgAZ7ct6FDENT09jZdeegmvvPIKenp6jnwQH/vYx/DTn/4UoVAIzc3N+PKX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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEGL_yeSN97v",
        "colab_type": "text"
      },
      "source": [
        "In vector form, we define:\n",
        "$$\n",
        "\\hat{y}_t = {\\bf w}^T{\\bf x}_t+b\n",
        "$$\n",
        "and we want to minimize the loss given by:\n",
        "$$\n",
        "loss = \\sum_t\\underbrace{\\left(\\hat{y}_t-y_t \\right)^2}_{loss_t}.\n",
        "$$\n",
        "\n",
        "To minimize the loss we first compute the gradient of each $loss_t$:\n",
        "\\begin{eqnarray*}\n",
        "\\frac{\\partial{loss_t}}{\\partial w^1} &=& 2x^1_t\\left({\\bf w}^T{\\bf x}_t+b-y_t \\right)\\\\\n",
        "\\frac{\\partial{loss_t}}{\\partial w^2} &=& 2x^2_t\\left({\\bf w}^T{\\bf x}_t+b-y_t \\right)\\\\\n",
        "\\frac{\\partial{loss_t}}{\\partial b} &=& 2\\left({\\bf w}^T{\\bf x}_t+b-y_t \\right)\n",
        "\\end{eqnarray*}\n",
        "\n",
        "For one epoch, **Stochastic Gradient Descent with minibatches of size 1** then updates the weigts and bias by running the following loop: \n",
        "\n",
        "for $t \\in \\{1,\\dots,30\\}$, \n",
        "\n",
        "\\begin{eqnarray*}\n",
        "w^1_{t+1}&=&w^1_{t}-\\alpha\\frac{\\partial{loss_t}}{\\partial w^1} \\\\\n",
        "w^2_{t+1}&=&w^2_{t}-\\alpha\\frac{\\partial{loss_t}}{\\partial w^2} \\\\\n",
        "b_{t+1}&=&b_{t}-\\alpha\\frac{\\partial{loss_t}}{\\partial b},\n",
        "\\end{eqnarray*}\n",
        "\n",
        "if $t = 30$, set $w^1_1=w^1_{31}$, $w^2_1 = w^2_{31}$ and $b_1=b_{31}$.\n",
        "\n",
        "$\\alpha>0$ is called the learning rate.\n",
        "\n",
        "Then we run several epochs..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xpzpw3wnN97w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "9424be7d-3772-45ed-e383-2f7bf3d7bee1"
      },
      "source": [
        "# randomly initialize learnable weights and bias\n",
        "w_init = random(2)\n",
        "b_init = random(1)\n",
        "\n",
        "w = w_init\n",
        "b = b_init\n",
        "print(\"initial values of the parameters:\", w, b )\n",
        "\n",
        "\n",
        "# our model forward pass\n",
        "def forward(x):\n",
        "    return x.dot(w)+b\n",
        "\n",
        "# Loss function\n",
        "def loss(x, y):\n",
        "    y_pred = forward(x)\n",
        "    return (y_pred - y)**2 \n",
        "\n",
        "print(\"initial loss:\", np.sum([loss(x_val,y_val) for x_val, y_val in zip(x, y)]) )\n",
        "\n",
        "# compute gradient\n",
        "def gradient(x, y):  # d_loss/d_w, d_loss/d_c\n",
        "    return 2*(x.dot(w)+b - y)*x, 2 * (x.dot(w)+b - y)\n",
        " \n",
        "learning_rate = 1e-2\n",
        "# Training loop with minibatch (of size 1)\n",
        "for epoch in range(10):\n",
        "    l = 0\n",
        "    for x_val, y_val in zip(x, y):\n",
        "        grad_w, grad_b = gradient(x_val, y_val)\n",
        "        w = w - learning_rate * grad_w\n",
        "        b = b - learning_rate * grad_b\n",
        "        l += loss(x_val, y_val)\n",
        "\n",
        "    print(\"progress:\", \"epoch:\", epoch, \"loss\",l[0])\n",
        "\n",
        "# After training\n",
        "print(\"estimation of the parameters:\", w, b )"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "initial values of the parameters: [0.52196897 0.36566595] [0.28672614]\n",
            "initial loss: 25.81812897591484\n",
            "progress: epoch: 0 loss 23.504504171059267\n",
            "progress: epoch: 1 loss 21.60229166021519\n",
            "progress: epoch: 2 loss 20.036138607550562\n",
            "progress: epoch: 3 loss 18.623482880589208\n",
            "progress: epoch: 4 loss 17.321126129796927\n",
            "progress: epoch: 5 loss 16.11331062791577\n",
            "progress: epoch: 6 loss 14.991030488968118\n",
            "progress: epoch: 7 loss 13.947498650098556\n",
            "progress: epoch: 8 loss 12.976920238854149\n",
            "progress: epoch: 9 loss 12.074088900124245\n",
            "estimation of the parameters: [ 0.90399594 -0.69126506] [0.46763101]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3Zda9YIN97x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "9aa424fc-d8f7-4031-bd6d-aeddcb9adae2"
      },
      "source": [
        "plot_views(x, y, w, b)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ikgmzYTMUAM3uFiwsXEJzc7N6bsuRGJ75xTv45EAPrFYrmpubMTw8jH379uV8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\n",
            "text/plain": [
              "<Figure size 288x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PleCc2X8N97y",
        "colab_type": "text"
      },
      "source": [
        "Note that the actual gradient of the loss is given by:\n",
        "$$\n",
        "\\frac{\\partial{loss}}{\\partial w^1} =\\sum_t \\frac{\\partial{loss_t}}{\\partial w^1},\\quad\n",
        "\\frac{\\partial{loss}}{\\partial w^2} =\\sum_t \\frac{\\partial{loss_t}}{\\partial w^2},\\quad\n",
        "\\frac{\\partial{loss}}{\\partial b} =\\sum_t \\frac{\\partial{loss_t}}{\\partial b}\n",
        "$$\n",
        "\n",
        "For one epoch, **(Batch) Gradient Descent** updates the weights and bias as follows:\n",
        "\\begin{eqnarray*}\n",
        "w^1_{new}&=&w^1_{old}-\\alpha\\frac{\\partial{loss}}{\\partial w^1} \\\\\n",
        "w^2_{new}&=&w^2_{old}-\\alpha\\frac{\\partial{loss}}{\\partial w^2} \\\\\n",
        "b_{new}&=&b_{old}-\\alpha\\frac{\\partial{loss}}{\\partial b},\n",
        "\\end{eqnarray*}\n",
        "\n",
        "and then we run several epochs.\n",
        "\n",
        "Exercice: explain the difference between the 2 schemes?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Q4PQJsrTN97z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "fc377862-07b4-4935-8cdb-b74bd35444db"
      },
      "source": [
        "w = w_init\n",
        "b = b_init\n",
        "print(\"initial values of the parameters:\", w, b )\n",
        "\n",
        "learning_rate = 1e-2\n",
        "# Training loop\n",
        "for epoch in range(10):\n",
        "    grad_w = np.array([0,0])\n",
        "    grad_b = np.array(0)\n",
        "    l = 0\n",
        "    for x_val, y_val in zip(x, y):\n",
        "        grad_w = np.add(grad_w,gradient(x_val, y_val)[0])\n",
        "        grad_b = np.add(grad_b,gradient(x_val, y_val)[1])\n",
        "        l += loss(x_val, y_val)\n",
        "    w = w - learning_rate * grad_w\n",
        "    b = b - learning_rate * grad_b\n",
        "    print(\"progress:\", \"epoch:\", epoch, \"loss\",l[0])\n",
        "\n",
        "# After training\n",
        "print(\"estimation of the parameters:\", w, b)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "initial values of the parameters: [0.52196897 0.36566595] [0.28672614]\n",
            "progress: epoch: 0 loss 25.818128975914835\n",
            "progress: epoch: 1 loss 23.160826482627552\n",
            "progress: epoch: 2 loss 21.533184257877707\n",
            "progress: epoch: 3 loss 20.024997737752603\n",
            "progress: epoch: 4 loss 18.622759538741857\n",
            "progress: epoch: 5 loss 17.31897884938732\n",
            "progress: epoch: 6 loss 16.10672438131725\n",
            "progress: epoch: 7 loss 14.979554182311105\n",
            "progress: epoch: 8 loss 13.93148083613598\n",
            "progress: epoch: 9 loss 12.956939314443526\n",
            "estimation of the parameters: [ 0.88577386 -0.7084342 ] [0.45411412]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRwv1U0iN970",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "2f8aaf67-c0ee-474c-97c4-749c340f38d5"
      },
      "source": [
        "plot_views(x, y, w, b)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8gmAwCIZhEI7E8OJPDuE3770NoijCbDYjGAxieHgY+/bty/ucT81E8UffP4Ws\nIEIQKPx8Koz/052Cz6FdaSS9ina7XddSR1213LNnj9JiNDY2htbW1rLzGG4k1Jy4yrX+rGfaj9fr\nrXm/olpyQP6odWAsy8JisaC9vX1DJQe1aPkhhYGl8DL+52e/woWJacyGIoouqtpR1EZECGo6LMxx\n0RTQas7AnpaQzmZBC2nEl9IwNDTAZrNhJUvhguiDnc+tJUMZM7rtAczPz6OpqQkURWE5msALrx/C\nR/ZsB8Mw8Pv9iEajRePJ/vGXY5BkGS4LB57nEU6I+P9+chL/z6dv0jxvkqwfGhqCLMu6EbnaD4wM\n75ibm0NTU1ORlfSNjE23VFwPXC4XIpHyDbZr6Vcs542lZ4E8MzOzYdN31KBpumq2NuRcY7GYcq5L\nyxFML0UwG45hPhyBy+UCx5kQDFrL73ATwcuJmEgaIMkyUiIFg5CFlExgIZJRKnEcx8HJceAsVlCU\nDYuLizAajZhJmUFBhIm5Rn+LkhVbzbnpR0RUGk0k8cO3juDeAz1YSfIwuoOYuXQmbzxZPCOApa8Z\nJTI0jaQgl1TfE1I6fPhwyUp14fAOsi2pPn8QKo0fKOKq9AMpF3Gt1xJYDYZhamLfvNalYqlzlWkW\nC5EkpkNRhKOJnBsBxSoOHhuJai4VCRFns1nw2SwsaQkxyQBOEtFopGHlOHA2i+bySxRF+Hw+zM/P\ng7K350dsyEVpDQ0NmJ2dRTweV7zEYokU/t+fjmLuzWWwLAOPxYBHohfxkavC3490+fDv70+CoSlk\nBAkMTeOBm7swNzeqqd0iIO0+hROFChEMBhGLxXDx4kUAyFPWfxCS9WsirmeeeQZut1sx8fuzP/sz\n+P1+PPXUUxW9/vXXX8dTTz0FURTxxBNP4Omnn877/fPPP48//uM/VqqJTz75JJ544omKj6+cUp1U\n+jbCErgQ63VBrRTlvOALR65pySt8Ph+cHh/GZ5YwMpVvD0OuZy1v+LW8V2GLD4kyyFI21yHBgaZp\nzM/Pw+PylC2EGI1GOBwO8IkF0HAhLlC4ejXQYc1F7oFAANPT08r7LGYNOJs0wcym4HXZsBjP4v/O\nmuEZHsbBgwfx+ZtawQsSXj09D5aj8bl9fhzocCPdeE19r9VoLcsyDAYDzGZzSSNDIDdpWy3L+CC1\nBa2JuB577DE8/PDD+NrXvgZJkvDCCy9gcHCwoteKooivfvWr+OlPf4qWlhYcOHAA999/P3p6evK2\n+8xnPoPnnntu1cfmcDgQiUSKfI0EQSh6aIm3kbqJuNpN07VyJlVHfqWiKLWjA0neLq3EcHFqFqMn\nL63ZHqbWUEdR5I8kSUqLDyHwHSyRAAAgAElEQVSpcvMzK/msKYqCw+FAKjWP7ZgDLO2QZSBoEeE0\n5GIwmqYRCOTyXc3NzYiJDGSZAiAjFInDabNiYjmDLVu2KNHS/76jA//7jg6cP39eMRo0mUyaPvME\npEJISGlhYUG3N5co8H/1q18hFovBbrd/YNqC1kRcHR0d8Hg8OH78OObn57Fnz56K3UoHBwfR2dmp\nuAs88sgjePnll4uIa63wer14//330djYCLvdrowkI5IDm82GpqYmRKNRHDhwYMO/dTZyKGxhxJhK\npTA4OJjn6KAVMcqyjPlwBMdHz6/JHqZWwzIIKouiGladuykkLlnOySEYnVvC5/MhMT6OZmNCU1DM\ncRxcLhcWFhZggg00ldsnIGMxksCOppy0p3D+YqG8we12o6mpCWfOnClqtCbRknqiEMmxakGSJLhc\nLmXSNpFl3OhtQWs+6ieeeALPP/885ubm8Nhjj1X8Oq2WHq0m6h/84Ad4++23sWPHDvzd3/1dyUGv\nmUwGX/7yl3H69GlMTExgYWEBv/u7v4u77rpLtz+PVOE2OmlerYirnEjVZrPBaDTqkrHaHubC5Cyi\na7SH2UiiJwp6IqmYn5/XjKKqpUlSE9dUksW5OAdJziXw+50ZGDR4kOM4LCwsKBKIQtjtdqTTadjS\nK2i3ODCZMoGmZLC0jK3UIsam57Fjxw7F9tnr9WqOMSON1lNTU2hruzYuTS2FUBsLHjhwQDOC4nke\nZrMZHR0dRT2UN3Jb0JqJ66GHHsIzzzwDnufxve99r5rHhE996lP47Gc/C6PRiH/6p3/CF77wBfz8\n5z/X3Z7jOPzBH/wBdu7ciWeffRY9PT247777Sr4HSdBvNHGtNuLSy7upPen1RKoTExN5D7QkSZhZ\nXMbo5CwuTs2t2R5mI1BOQc8wDHw+36ojAlHWj5i0QFEUlrM0Tsc4mGkZNAUsZRiciXEYcOYXVcgX\nncPhKNle5fV6MT4+jh5jBD0uGVmJgsOQ04798BeH8ak7D+RFS+UEpXa7XbHKKdzWbrdj69atynTs\nQkInROfz+RCPx3Hu3Dn09PTc8G1BayYujuPwkY98BC6Xa1UPfyUtPepl5xNPPIE/+ZM/KblPiqKw\nZ88eAOUnWhMQ4tJzmqwWSkVcJIqKxWJFA1zXmncj9jC5JuY5JFLV7xBYzVJRqw+xEgV9Mplc1X0V\nEyiMxjhkJAoWRsIOOw8zU9lxhjI0YjyNOAVwtAw7KyGcLX5vEqFZrVak02ksLy9rem9RFAWj0YiV\nlWUELWY4jNceM1GU8Movj+ATd+xT+hD18k0Mw2BgYCBvKKyW+LSxsVFTKwZAafkBcimekZERTE1N\nobW19YZuC1ozcUmShPfffx8vvfTSql534MABjI6OYmxsDM3NzXjhhReKIja1jc0rr7yC7u7uivfv\n9/tx/vz5stvVql+R6KsKRaqFebf1DHAVRQmTc4s4cm4CQ+PhInuYaqLUzV1pH2IlhLQacuQl4GzU\nCIaSYGNz+qxzMQ67nRmUehZlWYYkA5eSOcJjKRm8RCMjUmgxF0fJahsct9uN2dlZGI1G3SEYHo8H\n8/PzCAaDRZHwj98ewv+6bQ9aWlpw4cIF3etKhsISQamean779u04duxYnlYMyF9aqu1yrFYr3G73\nDdsWtCbiOnPmDD75yU/ioYcewvbt21f3hiyL5557Dr/+678OURTx2GOPYdeuXXjmmWewf/9+3H//\n/fjWt76FV155BSzLwu124/nnn694/z6fD++++25Fx7ERxFVYvSQWyJcuXVLcRavR6sMLOXuYi1PX\n7GFmZkMIBjd+QrdWRW8j+hArfW1apCABMF9N1ZgZGXGRBi8DXJldxAQawtUoLSNdXT5JFIJMDCsr\nCYiiCK/XqzzgallIIBDAzMwMOI4r+sIh7V08zyMUChWNJ5NlGa8eOoaP3bIbQG7loc5lqeF2uxEI\nBHDmzBlYrVbNwgBFUejv71dIyW63A0DRqoJhGOzevbvILudGS9ZTZb7ZNr58VGWMjIzg2Wefxbe/\n/e2S201PT0OWZbS0tKzpfco1TJM/VqsVx44dw8GDB9f0PmqUs4eZnZ1FY2NjVb81JUlCpqBZWhAE\nWCwWhaRITqqaIJbElSAtUji+YoSFkUBTgCABWZnG/oZ0yXzX9PQ0zA0BvLNsAyvx4GVAkgEJNG61\nLcFmzImHKYqC1+tVBqWoSSidTiMUChVFVWQ5BuRmhqrJpBBeo4zujiZ0dXXp2j7LsoyRkRFks1m0\nt7frznKMx+MYHh7G/v37wXEczpw5g6ampqL9rqys4OzZs3lmhrIsb8hnuQ7ofno3Br2uAqvJcZGW\nnXLQ82ivtGGaNCavhVCIPcyFyVlMzF6zh9HCet6nlD+72hOLYRgsLi7qPjjXAyZGRquFx2TSgJwx\nDYUdtmweaWktY3mehyG+DDfDIgQzGBqgQaPFLCDgdAKQYTabEQqFEI/HNdtlTCYTrFYrlpaWdK+J\n3+9XnCS0XCKOX7yCbZ2dOHPmjK7wlCzzfvnLXyKdTuteC5vNhs7OTiVZr7e0JJOHRkZGMDAwUJSs\n3+yVxutGXOXU85lMBo8++iiOHj0Kj8eDF198UXM6SiFWm5xXQ28s/Ho92oliudLXEHuY0clZTMwu\nQqywladSd1Itf3aiyCb+7A6HQ/N4JUmqiY5rtWgxi2gwSMhKFFgpC1rIYDl5jYC1lrGzs7Pw+33w\nysBkkkdcoOEw8GgxC6AoQJSAy0kOMa4VUiiEHQ7tLwWXy4W5uTlF5FmIQnGqmhSI++nh05ewq6NJ\nsXTWIg6GYeB0OjE+Pg6fz6dbWPL7/YjFYrhw4YIucQE5KRJxLtm2bRsoikIikcD8/Lzi6bVZcV2I\nqxL1/L/+67+ioaEBFy9exAsvvICvf/3rePHFF8vuezUN1PF4HFNTU0VRlN5Y+LWCSCJKEVcynVHG\nxk/Nh9bUd6hFXHqzAslDbLfbV1VR2kw3cyUEXDggQws0BaV1h0CWgffCZkylDFdjuEaElqO4pSGh\nuY9yURXHcWhoaFCcJNTnQEjq9Pgs2rx22M+exa5duzTfR5ZlbN++vewIs61bt+LEiRNIJBIlFfJd\nXV04evQo7HY7/H4/UqkUotHopq80XhfiqkQ9//LLL+Mb3/gGAOA3fuM38OSTT65pGaQ36YZhGOXv\njZ50oyeJiCfTuVabqTlcmV/f2C9JkiBJEmKxmLIsWstDvFmhpZ4nBGwwcFhmPViEGQYa2G7PwmUo\nTfzl7qW4QOFKygAGMmiagixTCFF2LMbD0EpDqaOqwnwXgc1mK5JRFPrNTy7FEInG4HQ6NfOvPM/D\n7/cjnU7jbAmCoygKfX19+PnPf45YLKbrwqq2y7FYLMhkMjAajUqlcbO2BV2Xu7gS9bx6G5ZlFTvf\nwuqMGktLSxgeHgbP83jkkUfwqU99Cjt27FByUeRm4DguN1r9+PGKE8DrAcMwShQYTaRyZDU5i5nF\n5TWRlV4URTzaVxtFrRYbuVQkFUtRFBEKhSrqQbycYHExycFAy5BECoNhE271pGBj9Y+zHHEJMgWK\nupYdpqicsaAEGtFoFA6Ho+g16pYfvX17PB7MzMwoMgqtQRnLaQk/+MkhfOHhjxf13BJJRnt7O4aH\nh3HlyhXdAhPxgjt16lTeFCCt4yYKfL/fD4vFknMC2cSVxs13RGvExMQEHn/8cfT398NiseBzn/sc\n7rnnHl2NjdZYqI1CMsNj6MwlzK3EMbe0UvHrSCd/YSOxXhQVCoVgsVhWPZhjNagmGerpvshSq9Ie\nxIlEzorZQAEULSMhUFjMMLCx+imDcsTlMEgwMxLiAg0WOUW+iRbR5DIjGlmB0WjUvM52ux2pVEpX\nakNRFBobGxUZhd6EnwTP4F9f+r/46uc/rZus7+3txeDgoDJ+rhAkr0qWlvv379e9lna7HZ1XCwTq\n/sjN2hZ0XYirEvU82aalpQWCICASiZRs5G5vb8fPfvYzAMDjjz+O1tZWXdKqBcKRuNIXeOHyeEmh\nIlBuKWRQ5kGWuoHKWdtcL6xFPT8zM1NRV8NkksVchoUkAwZahpcTAVAo95iVtT6igHt8CQwumxHh\nGTRwErroRRgYDoFAAHNzc0WJdgKn06no97R8y0g709zcHJxOp+Y+DAYDYoks/vF7P8LvP/qbYNni\nL1qirC+cq0hAEvNerxexWExp99GD3+/H6OgoZmZmFO0akLs3Kx3MWytcF+KqRD1///3347vf/S5u\nueUWfP/738fdd99d8bd9pZVFoHpTpmVZxtJKDKOTuWVgKHLNHkZt8lcNOxY9VHNY61qhN1G6WrMQ\n1YjwNEYiRthZCVGBRlaisJBhETAJaDSVbmyv5DqZaQl3eZNKRLu0JICmTTAYDGhoaMDCwkKeSl0N\ni8WCpaUl3WZsk8kEu92OaDSq6+xgtVqxEA7jn158BV965H5Alov2ZbFY0NXVpRlRFbb7lFtaArlo\nl+d5TE9Po7m5GRRFbSZdl4LrQlyVqOcff/xxfP7zn0dnZyfcbjdeeOGFive/mmk/60lAEnsY0sRc\naA9Doqh0Og1RFBGJRKpix6KHWhNXtSuWq0VcoCFTgIWRwdIi0gIFQaZwc0MaxjJ9iqW+sMh5ZTIZ\nJRoEkKecJ4n2lZWVomUaWaJ5vV7Mzc3pJuudTiei0WjJiVNutxuXpmbx/A9/gs98/E7NfJNeRFXY\n7tPb26so6/WEroIgYN++fTh69CisVitcLtemrCxetxzXfffdV+Tg8Bd/8RfKv00m06r7IAn8fj9m\nZmbKbke0XKshLlmWMbuUc1wYnZpDNJ4sG0WxLAuDwaBrx1stbBRxFZ4f+Ua+3hVLEyMBck66wNEA\nWBkGSoa5RFKegFynwq4AdQ6R4zhEo1GYTKaclXUB2ZFEu8lkylvWkryV2WyGxWLJ86MvhMViUSrd\nernJQCCA4XMXIUoS9nVqO1IQ2xp1RFV4b2s1bWtdF47jsHv3bmW7zVhZ/MAk59Xw+XwYGRkpu12l\njdaSJGF6MYzRyVmcH5tGOBJVbnhSLSwVRSWTyZrMcawGcVVi2pdOp8sO6V3Te0vAfIaFIAG8XPyw\nZKVcH6GZkcFQgNsgocPKYzxpAA0ZNIC9DdqqclIIUBMViaD0PjdJkmA0GrGwsIDm5uaiRDrpV5yd\nnc1bEqq3I370iUQCVmvxYBFZluF2u0t6fBGpxanzlxCJRtHb2wcTl3991BGV3W6H0+nMWyoSFDZt\nq89Hfe+ot7v11lvryflaoNIcV6lGa54XMDpxBacvTuD8+DSi8YQyLWW1pnabYVhrIQoT5plMpuJc\n20YsHQQJOLxsQoynAQrIio1wZWR4jbnrdjnB4lQ0F5EYaRm3uNNwGCTssmfRZhaQlQA7K4OjJfC8\nkEdQhYUAq9UKQRCQSqXKRsEkn0VMDQvPnWXZPBcIoHgZqvajL7yekiSB4ziFvPQ8vjiOg8ViwdTs\nIl584xB+895bYTHlR2gsy+Yl64mJYCHcbjcaGxtx+vTpvApi4fgyt9uN9vZ28Dy/6SQRm+JowuEw\nPvOZz2B8fBwdHR34r//6L801OMMw6OvrA5BziHzllVc097eawbCCIChDJCLRKC5OzGB0ahbTiyuQ\nqWv5Gp/Pt+YkJU3TNck96VUVq2k3U4iMCEykDEgINGyshDYLD+MavpznMyxiPK1oryKyhHMxI243\nprFyNQnP0blIKyNSGFw24W5vboYlrv4JraIQQCLJSkDyWYlEQjPysFgsSKfTCIfDcLvdRe1dNE3D\n7/crLT+FFjdkYIl6H1owGo3IZDI4f2kcL0oyfutjt8Jqzl/uqZP1DodDU28GXHNYVY9D04rQGhsb\nN1Res1ZsCuJ69tlncc899+Dpp5/Gs88+i2effRZ//dd/XbSd2WzGiRMnyu5PLzlPVPTEuC8UCoHn\n+ZzUgDPi9cGz4CUZBgMHrz9QlXMDaidTIKLBRCKRlzDfqLH3kgyMxjlkJQpGRkaUpzEa59Bjz4Je\n5e4FCXleADRk8HLuBwmBzvmUSCJ4WQZECcs8g9m5eZiMaysErLaa7PF4EI1GkU6ndaOYmZkZJJNJ\nJU+mBun/XFhYQCBw7d5SLytJzkxvWSmKIux2O2KxGCZn5vCfrx/CZz52G+zW/OPxer2IRqOYnp7W\nFVirvbnIODQt4tqMiXlgkxDXyy+/jLfeegsA8IUvfAF33XWXJnFVCqvVimw2i1deeQW7du1CJpNB\nIpHrMbNYLMoHZbVakUqllNYjxuzAq+8cW/f5FGIjlopasgMyvYVhmJKN0tVCRqKQlihYr0ZJFjYn\n/sxKVN7Q1Erg5kTQADISQMkiMmDgFyOYnQ0hJhrAS80573aGgshycDBAc0B7WVUJVktcREFeSuJA\n/LlMJpNmZOZwOJBOp/OU94V5M7XHl9ay0mAw5AlYX3jjEH7rY7fBacvXCG7ZsgUTExMIh8O6URdp\n9yHeXHXiWiXUjaeNjY2Yn5/X3C6dTmP//v1gWRZPP/00Hnzwwbzfnzx5El//+tcxOzuLhYUFvPba\na+jq6kJrayusVmvRzRSJRBCNRpX/79zSjPnwCo6evVzV81svcZUSp6qjjXQ6jVQqpami3ggwlJwL\nhORco7IkkyGplZEWIV+Sj2oTGEyKLkg0iwCi6HIwsJgCaKJpICrhfJyDQAEsBRxsWJ9//lr0exRF\nlcxFEWHp7OysrjbL5/MpzdhalUqGYcouK9UCVoZh8J+v/wq/de9tcDuvvSdFUTCZTJiZmUFDQ4Nu\nr6LJZFIspIPB4KYTmuqhZsT10Y9+FHNzc0U//+Y3v5n3f4qidG+oiYkJNDc34/Lly7j77rvR19eX\nNxBz69at+M53voPGxkYcOHAAzz33XMmIQ6uqeMeebiyEI5iar0zAWgkqTZqvV5xaax0XRwNNRgFX\nUqyyNGw2CzlpQgFKab6MRiOcTie8BgN2UhQACTMzUdgs15Y5Ox08Wi0CMhIFGytpvsdqUAlxaW1T\nzm/eZDKBZVlEo1HNJSWpRBJ9lxb0lpXq5DkRsC4uLoKi/Fcjr1vhdV2LrmRZRn9/v1JB1CMll8uF\nlpYWTExMKKsP9fFuRtSMuEg7jhZISbmpqemqR5L2gEtSgt+6dSvuuusuHD9+PI+47Ha74ofk8XgQ\nDodLGt5pVRVpmsYn7tiH7732K0QTGzcVp1Sf3lrFqbUmrqRAYTbDIi7QoCmgy55F0CQgkykm32po\nvqysDGuVTHnV/vFavysFks8ymUya5ESml8fjcc3Ii2j6FhYWdN+DLCsjkUieAFZ9zE6nEwsLC4hE\nchPHX3jjHfzWvbfC774WXdlsNuzYsaNoNFkhCHGFw2HdyuZmwqYQZ5D2HgD47ne/iwceeKBom+Xl\nZUULtbS0hHfeeadk35XX6y2rntfz7rKYjPjknfvBViE/RKIoSZIQDocxNzeH6elpLC4uKmPHXC4X\nmpqaEAwG4fV64XA4dPMkpVBL4pJkYDhiQDIrwSKngGwSpxd5TM3MIxaLQZZlWK1WBAIBNDc3w+/3\nw+VywWKxbIrSOommiGWxJEkQRVExSiz8vRokalpaWtK0K5JlGT6fD8vLy0rLUyGsVis4jis5c9Pn\n8yEWiymOp1oN2eptUukMXnzjEGaX8l1HfD4f3G43Lly4UPKaWK1WRKPRvOdms0Zcm4K4nn76afz0\npz/F9u3b8bOf/UxxQx0aGsITTzwBADh79iz279+PgYEBfOQjH8HTTz9dkrgqkUSU+lAaPS7cfbB3\nVechSZKSfF1cXMTMzAxmZ2cRiUQgyzkbYJ/Ph+bmZjQ2NsLtdivN09W4QTZSdsHzPBKJBJaXlxHi\nGbw5JWAiTiPJSwBFw2YywGS2wOlfH/luFAgBybIMURTz/iYkRVwQGIYBy7KKgwghM/W5qPVbhSDL\ne7/fj4WFBd38ZkNDg1Lp1gIhyMXFRYVUC68ncZsg26SzPF76ybuYmFnI+4LYunUrUqlUyY4SnufR\n19eH8+fPV2xrfr1w/b/6kFvWvfnmm0U/379/P/7lX/4FAHDrrbdWpIYnqLRfsRR6t7VhPhTByQvj\nRb8r16dXKDuYnZ1d92SfcqhGxEWsdNQCTrUhIc9aMUU1wG4xgc0wSMgMjJQEOy1Blihw9PW3dSbX\nQO9a0DQNm82GhYUF2Gw23QiQkBmJxgo/u0L9lvr9yWxFh8OBxcXFvFwVgSRJMJlMCIVCmpOCgNyq\nQI8gCViWzeuLzPA8XvrpO+htvXZMxFiQ2OBoVRp5nofVakVvb6+SF9uM7T7AJiGujUClxEU0VnqR\nwe0DXZiamcfE7MK6cjbkfTZSnrBa4hJFMY+g9KqV6gd2PMECEGBiAC8nYTHLYIVnwFDAFmvlQ1jX\ngrNRDsNRIyQZ6LJlsceVARlEpY6cCNSjxAqLPgzDwO12Y2lpCYFAQPcLhbxOT7Cq1m9p2RZpSSAI\nyP3gdrsVmxyt9yAESXJZWjCbzbBarYrZZjqTxdsnL6GrqxtbW3KkSZT1J06c0E3WUxQFp9OJ9vZ2\njIyMVGU61UZgc8TxAF566SXs2rULNE1jaGhId7vXX38dXV1d6OzsxLPPPqu7nc/nQzgcLvu+pLIo\nyzLS6TSWlpYwNjaGkZERDA4O4tSpEfR3+GE2cspk6bXkbGrR9qNHXCTPFo/H8/JsS0tLyGQyoFkD\nMhYfeFc7OHczPB4P7Ha7pt++gQKIUtTC5vyvAkYB+xrSaLNU5vW/FlyOsxhaMSEp5rRjp2NGjEQM\nZZd6DMOApmlNQiAtW2pJjBpk6R+Px7GysqK7FA8EAgiFQrqzDnw+n6YLBPnCJJPLS33RNjQ0QJZl\nRY+oBZfLBUEQEIvFcstbAD966zAmZhfzzpkYC6rvx8LzCgaDsNvtm3bJuGkirt7eXvz3f/83vvSl\nL+luU8mQDYJSEZckSYr/fCqVwvDwMGRZhtFoVIa2BgIBmM1m5YZvbG7BSz99r+KJO4WoRdsPierS\n6TRiaQGJrAiZz8BICbptMJIMnI5yiAo0GABXZGCLxKPZrJ009psEGCkBMd4EXLUz7nVmS9okV4oI\nT2M5S8NMi5Bl5D1YY0kDBPmauF6QgfGUEQMNwrqW30StTpZERFdW2FzucDhgMBg0o2ZiYaPuV1RD\nLYFQmw+qI/1yk4JIw3c4HNYUpxKQoR3BgA87uzrwazftRcDjKtomGo1idHQUXV1dAKBMslajs7Nz\n0+q6Ng1xdXd3l92mkiEbBIS4lpaWQNM04vE4YrGY8g1itVphs9lgsViUal4pBH1u3LW/F28ODq/h\n7Dam7YcIU9VOFYIgYC6axRLsuWiDY9FoEuHTMdaLCTSiAg37VeKRZGAyaUDQJEKLDww0sJ1ehMFh\nhChRaOBERTm/FhAyH4uzeG/ZAorK2dT4ZT8CV0mCoigYmeLpoBy9ehEpmR9JrhlpLp+fn4fNZoPJ\nZEJDQ4NmwUSdqC/8HbGwCYVCmsekbtYmcoPCFEWpSUGFHl9a7qsNDhu62oNo+ejNGBs9h23btqHR\nq+27tW3bNpw4cUKRIemp5jdrVXHTEFclqGTIxvHjx/Hiiy9iaGgIIyMj+PznP49vfetbsNlsaG9v\nh8ViyfvASWWpEvRvb8N8aBmnLk2V37gA61kqEieHQksWIkxV59muTM9gxeiF86r1iyzLmM8wcBlE\nGDXSa7nRW9dAXf1Z4c/VYCkZTWUcRrXOQevfQI4s31uxQJYBULmRrvNUAy4vXUFXU87Ibrcri4mk\nAULOggsGCjjgLm0VRJbIapKSZTnvupG2qEQigUQiAZvNVjbfRYSghds1NDRgZmZG93MuNB8sJK5S\n8xfJexqNRmUgR2NjI9xOO7o6guhqD8LrcijHFAktYHp6Gm1tbZrnU5is34z2zKVQU+IqpZ7X0m6t\nBQaDAXfeeSe++tWv4oEHHsBrr71WdvtKPLmA3Id998E+LEViqxp6AVROXOsVpkpXZzmTKc7U1Uk1\nokwoKR82RoKRlpEQckr4lEih0SSUbZIupTwvV9UjryPnwEs0ZJkCRecfc5biEIvFci4HBhkPNSdw\nMZabc7jFKsDFXbueWn5bABSCItdNrzhis9mQSqWU99MD+RwJ6RReA6/XiytXrugaVKqn/GgtO8n8\nxUJbaDXJbWltBidn0b21FXv7tceTkTkFFy9exPbt2zW3YVkW/f39GB4eRnt7+w3TpwjUmLhKqecr\nQSVDNnp7e9HbW7n+ymAwlBxpXgiWYfDJO/bje6/9Csl05eaAWjkuLUnFep0cGMgw0zKSIgUzLSMr\nUWAogNOp9rE0sMuRxWSSRVqi4DOKaDGXTrKrj6cUSZWq6qlhomWYGRkJMXeskgwwFIV2jwXRhWmY\nTCZwHAcbK2PAlVGqoeF4Jq8aSpZYTqdzTZbRalIpZeVClv16VWKTyaTZa0heSzpFzGazJrlp2UK7\nbBb0djTh127ZB6/LAUmScOTIEV0bHJ7n0dnZiXPnzmF+fl5TjkHeq7OzE+fPn0dbW1vJ67OZcEMt\nFSsZsqGGVom8EAaDAbFYTPf3WnBYzfjEHXvxgzcPVxRFkbyKmqiq1QZTCAkUPEYB8xkWSZGCiZbR\nahHAlniGTYyMHfbSUWfhUk8UxSI30JhA41TUiJREo8UsosvOV2RvQ1HAvYEkfjpvRkqkwVAy7vCm\n4TAAzNWktcViURwwWJZVSIpct2oJeIloNBgM6ka1an1X4f2lXoouLS1ptpwR8erCwoLu5CePx4Nk\ndAUDnS3Y37sT8UgYFEUpvYhqZwctG2ae58FxHPr7+xWfeb3Gb7/fj7GxMYTDYcWbi5znZsWmIa4f\n/vCH+P3f/30sLi7iE5/4BHbv3o033ngDMzMzeOKJJ/Dqq6/qDtnQg8PhQDQa1e2MB0q7oJZCa8CL\nO/d2462h03k/L1yykOoUeQhIFLURivKkQOGy5IEpntM6NRoFdFgFzSR7KZRb6pnNZsTjcWWQAkVR\nSIkUfrZoRlbKjQZbyo1VrAIAACAASURBVDBIi8CehsqurYMV8UlvGPF0FjKfAb+SxfTyNeGrIAgI\nBAIbPnGGRGxLS0u6PbOAfr6LLOlIlVCvX9FisYBhGMTj8TzyavS4sKM9iB3tQZg5FkePHoXFyGL5\nqjhUDZPJhJ07d2raMJOlKsllDQ8P4+DBg7pfjjabDbFYDHNzc8oStU5cFeChhx7CQw89VPTzYDCI\nV199Vfm/1pANPZC2n1LEpdevWA6SJGF7sw/nLlpx5vJUkUUwyauQpUA6nUYymdxQN8nLCQNkOQsL\nk4sC5zIs3EYJzhLj6Fez1CP/JqJLq9WqnM90igEvUYprgyQDF+JcHnGNJ1hMJlmYKAHbTTGATxf5\n2huNRnDmnPCVPIiyLGNubg6ZTKYmszLJUFc9aQKBVr5LnYtSVwm1loQkv2o1stjf24Ud7UG47Pnk\ntHPnTgwPD8PhcGiSjsfjQSQSwfnz54sq8+Qzczgc6OjowMjICHbv3q1JSDzPo7u7G6dPn4bVai15\n3psBm4a4NgJEElFo1aFGJcl5QRAQj8cVSUUikYAsy7BYLLippwPRZBqxVLZkNLCRAtTkVfO+KE/D\nQOXeQ0lwS7mbtFRVL7d9Zfkoso3P58tbUmltTeGaTc+pqBHnMlaIoEGBwWjcgP/lXkJDg7VsHo+8\n3+zsLIxGY03m/Hm9XiXfVaraVpjvUvc0lrJsDvrcaLSxuOe2gxi7NIq+bS2aThMejwcrKyuKbEEL\nW7ZsyZM2aCEYDCISiWB8fBxbtmwp+n02m4XFYlGS9Zu53QfYZMT10ksv4Rvf+AbOnj2LwcFB7N+/\nX3O7jo4O2O12RSGtp7SvpNGaYRilQ1+WZWQyGcRiMYWoiIMDEaY2NzfDarXmPTweXwDfe/0Q0hlt\nJwBg4+ybZ1IMxpIGUABWeBqUZIRdliHKFCTI4CBAkordDcjfKzyDEytGpCQKPk7E7oYsOBWHyHIu\nmooLNJwGCY1X9V0kvxQOh+H1etFk5GGgDMgIFGQ5d57tdBjLyzFwHIfz2QDEq40aMmjwoLBIueDm\nKltKsiyrWME0NjZu+DKGpukictZCYb6rcKCGul9xT283utqD2N7WBIfNgvfeew+Nfi8sJg4nT57E\nwYMHNd9n69atmJyczLO4KTwGtbShUPJD0NXVhaGhITgcjqJxaUSAajAYsG3bNgwPD+Omm25a7WWr\nGTYVcVWinif4xS9+UVY0WsraRq2eT6fTOHbsGARBUNovSHuPWj2vB5fdivtu34sf/vxwycbeaivn\n0yKFsYQBFkYCRQGMUcIMb0AkI8PA0thizsLBARRF55EVQUqk8G4ol9Q10MBsmoEQNuJWb65aKsvA\nYNiI8SQL+arL6U57FrvsaUUTFY/HkUwmwTAMDnJmjItu8JQBrVYJ2+0WUFRuaSetFFxD+arP/CpA\nrLbL5S2rBUI6oVCopK+bOt+lrjRSFIVmvxs7DvQhEwuhvaU5rwpOojOXy4XGxsaiga7q/VssFkxO\nTsLj8Wj60bMsi76+PoyMjKC/v18zWiIJ/aGhIezdu7cowiP3RiAQQCKRQDKZ3LRLxk1FXJWo51cD\nv9+P0dFRxONx8DyvRFKk34tUWliWRW9v77oEeB1NPty2eycOHT+r+ftqLBUL81EZkQYgK8l3Ew14\njECzOIeOgA8sTQHQX1YtZ2lIAMxXNzEzwGKGgSjndGARnsJ4ggEDEZBlSKKEU8s0HIlF2Iy5yp7f\n70coFEJjYyMYhkGuoF4cRXVYBUwkWIggBAq0rqG3keTXzGZzTQSTJN+ll2QnUPJdooi2Rh8ODvRg\ne1sjbJYcOQhCOwYHB+FwODTJoL29HSdPnsTMzIxm25AoikqS/cCBA5r5Lrvdji1btuDMmTO6uVSj\n0Yienh4loU+Wt4XYunXrphakbiriqhQUReFjH/sYKIrCl770JXzxi1/M+/2hQ4fwxhtv4M0338TU\n1BROnjyJP/3TP4XNZtP0n19cXKxK3uRAzzbMh1YwOjmrecyrmXlI/taSc5D/W1iApSkIyCXF0yIF\nq1GGU6SRjJcWUgKAgZYhy+S4ZPCiBFkCwktLyGYzWBE5yFILQAEUTYFlWECm4PE3wW64di6iKCIU\nCpWswt3pS+MwbcRUioWJlnGrNw2HYfURaKWShWqBoih4vV4lv1YYyZAqssduhs9hhbPFgQP79hYt\nxdQRkVZ1j6JyA10JuWmRpNPpRGtrK86cOYO+vj7NlUBTUxPm5uZKahMbGhoQDAZx5swZ9Pb2aopl\nN3O7D3AdiKsa6vlDhw6hubkZCwsLuPfee7Fz507ceeedyu/T6TQOHDiA22+/Hf/+7/+Ob3/72yX3\nRxL06yUviqLwsVsGEI7EEYpUpg2rxDuqsKpHwADY5eRxLmZAXMhNeO628zDR16ISrSUDedgMmSzs\nkg2hrPHqkAsKPZYYbDYrOK4BforB6CyDrJSLwHiJgpWVinoTiYtAqaiEoXB1Cbr+id6F+bWNhrqJ\n2uVy5Sqh2SzcdjM6mrzYuaUTAZ8XdrsdJpNJ8e8qvJ/sdjva29tx+vRpZT6oGoTcSkkXWlpasLKy\ngqmpKV3BaGNjIy5cuIBQKFREoAStra2IRCKYmpqC2+3e1NGVFmpOXOtVzwPXvOf9fj8eeughDA4O\n5hHXRz/6UQDAzMzMqqxtCkV8a4HRYMD9v7Yf/+e1Q8gWVCvVLpxaUFf0Kv22cxhkHGjIQpRzKvgc\naHg8HiwuLsLn8+VNqxaEnJsCqZbd4hMQkgzISjRcnAg3d+0ayBJwmzeFo2ET4iIFr1HEzZ6MpqiU\nVOHIsIiNhsPhwPz8vO4MwvWgsDc0k8koRoJmRsZHbtmN3h1b0OB0aH5OoigW6fcImpubsby8jKmp\nKd3lHiG3/v5+zUi9u7tbicy0JjrxPI+WlhacO3dOM5cF5O61np4eHDlyBABuqHYf4AZcKiYSCUiS\nBLvdjkQigZ/85Cd45plnNLf1er0VT7ReiwhVD06bBR+/dQA/+sVg3lKPpum8sHy1JKUHisq1+mSz\n+Q9bNpvF3Nyc4m9O8nkSKKRFCkZaBksDLZAAqL2ZgJMrBoxcHXnvNoj4ZFMSphIBKcMwClnWoupH\nJBJEsrBWslTPpyTXjDi+Go1G2KxW7OnZge5trdjaHMCFc2cRDAbhdukXB0jeiHzWheTV3d2N999/\nX/eY1eTW1tZWZDnDMAwGBgZw/PhxTUNA4mTa09Oj5MS0ltRkP4cPH74hBmSosamIqxL1/Pz8vCJU\nFQQBv/3bv42Pf/zjmvsjiutyWA9xqYcrqL8Z2xu9uLlvOw6fuqiQk9vtVqaorOfBLpxHWPiwmc1m\nRdU+MzMDu92ukGU4S+OXiybwEgUKMm5yZ9BmzXd6uJJicCrKgbp6PqEsg3eXTLg7ULqn02KxIJFI\nlG1UrhZWS5blGrGJ4yvLstgS9GF7WxDbWhth4q4tt3ft2oWhoSHY7XbNSIaApmnFZqiQWBiGwfbt\n2zEyMqLbjE2iKtJpUbiNxWLB9u3bMTw8jH379uWdezabhcvlQkNDAwKBAM6ePavbYULmICwsLGD7\n9u0KwW32iIsqkzC+/gbi68SePXvw9ttvl9xmenoasiyjpaWl5HZqrU4h1A6c5N+yLOPltwZx6co1\nv/ClpSXFmK4SFHpuFanMrzZk6yWpU6kUVlZW0NjYCBkUXp6xgJdy8gdRBiSZwieaknl5q2PLHEYi\nBqW/UZYBAwN8plXffVN9jWZmZhAIBGomYAyFQsq0JOBaL6U6+lQ3YquvG3lAGYbGlqAfO9qD2Noc\ngJHTP/aVlRWMjo6WHPcF5K4FIa7CfNfy8jIuXrwIlmV11ezJZBLHjx9Hd3c3pqenNfNiFy5cAEVR\neQ4Qw8PD2LJlC+x2O2RZxsmTJ5UhLVoYHR1FKpWCwWBQKvtEI3mdocue1/3INhpGoxHpdLpk/spg\nMORZ4hJiIopo9U1FSKnwby1QFIWP37YX33vtbSxHc/t3u92Ynp4usnwmS4tCY0CtYbCr+TY0m81K\nFMRanOAlCoarAy2IX1eUp2Flr0VdNlYCfdXQj6Jyi0grU5mUg6ZpeL1eLC4urjuyrASyLMP+/7f3\n5eFtlWf252qxZVmSZcuW9y225DXeHRJSkhDWBDBToCEBshBKCyQzYelA2UrohLIE2jKEmekUCFBK\nQinz/JLSYAIBSmlD4jWxLTt2HC/xvsqybMvavt8f5rtIsjbLsrxE53n8PLH1Wfdaufe97/e+55xX\nLEZPTw8ru7L83KirrT0hNo/LRXLsVLBKjpE7DVaWkEqlCA8PR3Nzs0PLGACsjbS9epfRaERISAiM\nRiPa29utxM0UQqEQqampaGxsdMhbUygUKC8vZ+uZAKaVI2i3UiwW231g6vV6xMfHo7W1laVjLPSM\na8kHLsqed/S0oYRBS+8rCoZhWM9ySxnHTCAI4OPmtSvwx0++hsE45ahA/ZZEIpFVXYW6HggEAtZe\n2RsXEF8sw7leDcJhBghheVpmAhBMdQotkSIy4sIYH4OTU38rnwOWlOrW3ywQQCAQQK1W25327Cmc\nGQMKhUKMj48jOjraaabH43KxLC4SyoRoJMdGIoDv2S2QlJSEqqoqDAwMOO1sOqp30eCamprKbgnt\nFdojIyPR2dkJrVZr9/0ZhkFubi7KysogEokQFBQ01TG2+AzokAxHk3v0ej0CAwNZ9r1YLLZrlbOQ\ncEkEroGBAcTGxjrc6gkEAnaCCs1ovMUP0uv1gEmP5YlylJ48w9ZVzGYzJicnIRKJEBgYOGd8pO4J\nLv7WL4CZBOPcoBkhAQQTJg4IQ0DAIFuin8an4jLAtZET6NVxYSRAeKB5xtN7qBuoUCj0SFhOPx/L\nQAU4NwbUaDRQq9XTWO58HhfL4qKQlhiDpJgI8L2wBaKZTEVFBWv57Aj26l00cHE4HOTk5DgstANT\n13BLS4tDekNAQACysrJY2RAhZNrWNDg4mJXyFBQUTKuJ0doe1SquWrXKK132ucKSD1xjY2Ooqqpi\npRT2tnoCgQDR0dHo7OxESkqKR8ehU4JGR0cxOjoKjUYDnU4HPp8PsViMtKRY6AkHNc0drDykq6sL\nYWFhc0qiPDk4xdHicxgYzYDGABTLJhHCJxByzVZEUktwGCDawcAMd2BPiO0Irup47loBUT7Z2NgY\nQqUhSImLgjIhGoleCla2CAgIQFpaGurq6qYFA0twOBzweDwYjUaW32U0GtmATgvtNTU1dt/HaDQi\nMTERDQ0Ndr23ALDT0Ovr7Ss3gKnsTa1Wo7m5GampqezPLTlnIpEICoXCI8cUX2LJB66SkhL87ne/\nw44dO5xmUgkJCSgrK0N0dPQ06xSDyQwuhwHHwnOJ1o3ol8FgQFBQEMRiMcRiMWJiYqYNgI2KisLo\nhB5t3f1sMXloaMipDm62mDQzrOiHy+PAaDTDaDIhUjz3fRdKFB0eHoZMJrM7qIK6vtKCuSd1PApB\nYADWrMjDpGYI16+/AiIv87vsISwsDMPDw7hw4YLTh55tvctoNFrxz+RyOYaGhtDS0jLNzcRgMEAq\nlbIWN0VFRXav44SEBJw5831Wbw8KhQIVFRVWNTHAuosYERGx4AmpSz5w3Xnnnfjiiy9w9OhRu35f\nFBwOB2lpaWhoaEB+fj4YhoHeaMY/zw+guVcNo16PZWITJMwUJYB6FkVERGDZsmVuddA4HA5uuKIQ\nfzz2NUa04xCJRKwDhbPW+mwQGWhCt44LPqbcUbkcDrhjAyAhYXNagKX1KIZhWCE2ACvXVzqoYjbn\nERjAR2p8FJSJMUiICgePy8XQ0BDOfUe+9EWRedmyZaisrHRoo0xhWe8yGAzTunZKpRJlZWWQSqVW\n72M0GtkpQbSjSceKWYJhGCiVSvzjH/9w6CVGhdbUFdXRdnChF+eXPB0CmKIgrF+/Hp999plDtTu1\ntKmrqwOfPzVotLJjFH0TDKJDheAHBmHCzMNNebGIkMwuyPQNjeBQ6TcwfsewpvP45mLLqDMB3wwI\n0Kvjgs8BikMnIdb1zIiS4QqueGU8Hg/Dw8N2R2p5AkEAH4qEaCgSpoIVlzv9PZuamsDn85GUlDTr\n47kDnU6HqqoqFBYWOs1WKEWisbERycnJ0/4PdDodKioqUFRUxG4lz5w5g5SUFIhEoqnrsrIS8fHx\ndrWhY2NjqK+vh16vd+qppVar0dDQgJycHKhUKisLKbpNXwBwGD0vicAFAP/7v/+L+vp6PPfcczCb\nzRgfH7fa6un1eggEAgiFQvT19SEnJwdftoxBGMAD/7sbo3dUh5XJYUiSzd6Fs76lA8e+qQQwdRGZ\nzeY57eSYvxumyjCz41rZ40fRC92SW2YboEZHRzExMeFUiO0MQYEBSE2IhjIhGvGR9oOVJcxmM8rL\ny5Genu4TMiww9YBsb29nM3bb8xkfH4dGo4FGo0F/fz+Ki4vtypX6+/vR2tqKoqIiMAyD8vJyZGdn\ns9mRXq9HWVkZ8vPzp5U1hoeH0d3djdDQUPT09DjkiAFAe3s7BgYGWAY9BYfDWSgmgpcujwsAvv32\nWxgMBnz00UcoLS3Fpk2bsHHjRojFYshkMiQlJVk9YcRiMbq7uyEJkkEzYURI0BSZ1EyAAJ53sqKM\n5Dj0DqpRUX8BISEh6OrqwuTk5JxZO1vqCzmcKS3jwMCAQ8Y5rUfZFs0trakdDU61B5FIxBbO3dUW\nCgWBSI2PQlpSDOLkshllaxwOB1lZWaipqWHtW+Ya4eHhbL0rPDzc6sFoNpvZ8oJcLkdSUhK4XO60\n2YrAVI3JsohOt4oUAQEByM7OZuU8ln8bpV1ER0dDrVY7dDwFpoTWvb29rJHmYsIlkXE98cQTSExM\nhEAgwNtvv42PP/7Y6c1G0/HI+GSUd01CZ5yiUSwLD0ZxkpQt0s8WZrMZH35+Eh29g5icnMTAwIBP\nyX+UxS8Wi+2Kii2n6dAt32zOjXZSY2JiHAaSYEEgFIkxUCZEI1Y++45rZ2cnRkZG7Br0eQNGo9Eq\nQNFp6aGhoZDJZKxFjT0WOu3c2euYEkJQXl6OZcuW4dy5c7j88sun/X5bWxu0Wq2VnKejowNGoxFJ\nSUnsCDOFQuEwm+/o6MD58+eRn5/PklwXCGse8G8Vv8fu3buRl5eHO+64w+k6rVYLlUqFnPwCaHQm\n8DgMQoWedbucYVw3iT/89W/QjuswNDQELpc7p+6etqLi0dFRtmBOA9Rc+rrT7VJkZCT7WQYHCaBM\njIYyIQYxEY6H3XoCQghqamoQGRnpcLagu9Dr9VZ0l/HxcXA4HLaTTIPU5OQkqqur3a530SzWFpOT\nkygvLwchBD/4wQ/s/m1nzpyBXC5nzQdbWloQGBjIfk9rZo5oFK2trTCbzeju7mZ5ZP7AtQChVqux\nZs0alJaW2mUqW6KpqQkCgQDx8fFzek7dA8P44Pg/YDAYvarzcyUqptOUR0ZGfOLoQNHf348wqQTF\nyzOgTJwKVnN5bIPBgPLycuTn57tFqrScPUCDFNXy0QAlFosdersDQF9fH7q6upCbm+v0b3OmZwSm\ndJiVlZW46qqr7B7LaDTi9OnTWL58OcRiMRobGxEaGmpFdRgcHERzc7NdGgVdD4Ctq/H5fJ9srd2A\nP3BZ4t1338XJkyfxyiuvOF1nMpnYIuhcjhUDgNrz7fj0ZLWVKNrdm9mVqNiyaG7vPWcq/PYUkmAh\nlInRSImLxMULTcjLy5szGogtaO3JliJBCGFHkdEgRWuNlkHKndkDtjh37hwEAoFdHaIlqFe9vaaG\nyWTC3//+d8TExECpVNr9/dHRUdZZtaGhAXFxcdMeyhcuXIBer0d6errVz2traxEfH4+QkBA0NTWB\nEILMzEx/4FqIIIRg/fr12LdvH/Lz852u7e/vR09Pj11lvrfx+amzONPYiv7+fggEArvUDVdibBqg\nZkLinEtHhxCREMqEGCgSoxElk7LnpFarcf78+WmWLHOJpqYmmM1mdlCwLXGYBqnAwECvnBPtbKal\npbnc/juqd+l0OtTW1gKY8qV3RFbu6upCX18fzGYz0tPTp3UbCSGoqqpCTEwMO/AVACorK5GRkYGg\noCC2tpuamupx99fLuLS7irZgGAYHDhzAT37yE3z66adOayrUrM6ZDa63cGVRNvqHR9gidlBQ0LRM\nymw2s/wob4mx3ekyzgRScTAUCdFIS4yBPCzE7vtJpVJIpVK0tbXNCdfKbDazczBpkDKbzdDpdCCE\nICIiAsnJyXPKV+JwOGz3r7Cw0OlDwZF/F+0oZmRkWAmpbRETE4Ph4WEMDg7aPY7tCDNqsU11inRN\nbm7ugtYoUnD37t3r7HWnLy5myOVy1NfXo62tzWXWJZVKoVKpEB0dPWe6wqnulAaiQA7ONDRjQjeJ\nkZERtl0eFBTEOgjQi5duLbziIMHns91ET7bFoZJg5CqTsL54OX6Ql46kGDlEQoHTc5NKpWhqaoJE\nIpnVVtxkMrHcqI6ODrS0tKCrq4vVilLKS0JCAuRyOdra2txWO8wWfD4fPB4PbW1tkMvlDj8PyxFn\nlm4kdExYVFQUxGIxVCqVw86zTCbD+fPnER4ebje4UZlZTU0Ney3bWupQXeUCYc4/6+iFS3KrSKHV\narF69Wr85S9/cTl0oa2tDUaj0WMRtiX0ej2bBdD2uWV3SjtpwrF/nkF3Tw8kEolPxs4DM98yykLE\nUCREQ5kYg3Cp2KOLXavVoq6uzqG9sC3omDn6+Y2NjVl9dmKxGCKRyGmNhs4iyM7OnvH5eor6+np2\nypQz2Na7+vr6MDIywvp+tbS0QKfTORzl980334BhGIdOE8AUBWJwcBA5OTk4efLkNKqFo1roPMC/\nVbQHkUiEp556Cs888wxef/11p2vj4+NRVlaGqKgotwmU1DHCMkjpdDqWO0W1jsHBwdMuFJ3RjE//\naUR3dzcEAsGcj+EC3NsyyqRiKBNikJYYA5l09sNCRSIRoqKi7Jry0c4e/fzGx8fB4/HYWlRycrLT\nzp4jREdHY3BwED09PVb1nrmEUqlEeXk5pFKp0yGrtv5dtn7z1Aest7d3Gr2DDphNTU116DQBfD8p\nqK2tze7ouwUStJziks64gKn/7A0bNuCxxx5zOXJcrVbjwoULDiUdrhwj6Pgqdy4MQgiOnzyDf1bV\nQq/X+2QMF4VtlzFcKkFa0hQpNCzE+5ONaRE7PDwcZrOZDfC0s0e/hEKh124qg8GAiooK5Obm+qyz\nOTY2hpqaGhQVFTnlSdHxZhwOB93d3eByuVa24gaDAadPn54m+TEajaioqMBll12Gc+fOgcfjOdwh\nmEwmnDp1ChwOBytXrmR/voB0isBi7yru3LkTH3/8MeRyOdthsQQhBHv27MGxY8cgFArx9ttvo6Cg\nwO33b2pqwl133YXPPvvMJfFOpVJBKpVCKBSyAUqr1VpJOujXbC8Ao8mEQ6XfoLq2HmFhYT4rmprN\nZkxo1Fi3qgjZyiSESRxPcJ4pCCEsCdVSJxoQEACtVou0tDRIpVKvdfacgXY2CwoKfJLRAt9vU7Oy\nstzid128eBEhISHTsquRkRHU19dbSX4mJiZQX1+PgoIC9mGQkpLisKnU19eHmpoaXHHFFey1uoB0\nisBiD1xff/01RCIRtm3bZjdwHTt2DK+99hqOHTuGU6dOYc+ePTh16tSMjvHUU08hLCwM9913n9XP\nDQaD1U1GLVoiIyMREhLCBqm54r1oxsZx8P+dQGv7RcTGxs7pzRwpk0L5nesCMU6itbXVbnbpLmgW\navn5mUwmCIVCK/oBvWm6urowPDzscCLNXKC5uRkAvFK7dBd1dXXsNGlHoPyyxsZGJCYm2s24bSU/\nIyMjaG9vZ6k7lHnviDVPmxkmk4mlpSyWwLUoalxr1qxBa2urw9ePHDmCbdu2gWEYrFy5Emq1Gt3d\n3TOaFffEE09gxYoV4HA4CAkJQVpamhVbWiwWIykpCcHBwejp6YFGo5lzRj0wRdq85apV+P2fh73u\n4Q4AUTIplIkxUCREQyq2rN0Fo6+vD52dnS6nHwFTWw9bzR4hBCKRCGKxGJGRkUhJSXF6U0RHR6O/\nvx99fX0+4xElJyejsrISarXapZLCW0hPT0dZWRkrEbKthVISrEAgQHBwMIRCoV0xNjUOpPpPg8Fg\nleUHBgYiIyPDofkgNSg0Go04f/48FArFoqhvAYskcLlCZ2enVRCJi4tDZ2en24Hr/vvvx8mTJxEU\nFIQjR45g9+7dUCqVDtnS0dHR6O7uxsjIyJzqCinio8JRcuXleP8vn7HDXWeDmIhQKBKmglWIyHHH\nUqFQoKysDDKZzKoORDt7llkoh8Nhg1RsbKzLzp49MAyDjIwMVFRUQCqV+qTWQl0kzpw545Jr5Q3Q\nIBUZGYmKigoEBwdb1UJDQkIQFxfHbpVpvcvefEbLCT4SicTujMawsDCEh4fbNR+kCoGkpCR2UtBi\nGQy7JALXbPHyyy+zncKbb76Zfco5AsMwSEtLg0qlQnFxsU+eUoWZKWjt7MHJqtoZj/1iGCAmImyK\nupAQA3Gwe8VoLpeL5ORkVsir1WoxNjYGHo/HbvUSExMRHBzstRpRQEAAUlNToVKpXOr8vIWgoCAk\nJSWhoaHBqwoJQgjbsLFk6guFQkgkEkRGRsJgMFiZ+NnCciqQpTc8BY/Hw/Lly1FTU4OoqCi7gTc5\nOdluJ1Kv10MkEllNCqLlj4WOJRG4YmNjcfHiRfb7jo4Oh+PI7MGS3vDqq6/i1ltvxYkTJ5w+8UUi\nEcLCwtgx6XMNhmFw8/qVaO3ogkajcZnpMQwQGyFjt4EiofPCvqNhH/QzmJiYQEpKilc7e44QERGB\n/v5+dHd3O60DeRNRUVEYGBiYcYmBwrKrTIMUredJJBKEh4dPI71S5wpXx6SkUDpGzPYhIRaLER8f\nj7a2NqshGBSUNV9WVsZ2Z4Hvx5IB308K0ul0/sDlK5SUlODAgQPYvHkzTp06hZCQEI9T3qSkJNxy\nyy14/fXX8dBDuwtHmAAAIABJREFUDzldm5ycjLKyMkRGRs65CBsA+Dwe7r7lerz0+0MwBgdP64Ay\nDBAXGf5dgT0awUH2gxXt7FkGKeoASzMpy2EfVGzuLZa+O6C8p9DQUJ/RFdLT01FeXo6QkBCnGTeV\nE1k2HcxmM7tVlsvlSE1NddmhZhgGmZmZKC8vh0QiccoPpGx62/mMFLGxsWhpaYFarbZr38Pn861G\nmNFZopYPZ6lUupAK806xKLqKW7ZswVdffYWBgQFERkbi2WefZYe33nfffSCEYPfu3SgtLYVQKMTB\ngwedpt+uMDk5iVWrVuHw4cMuC9M0M8jJyfH4eDNFtaoR7//1K8gjI8HlcBAXOZVZpcZHTQtWjjKB\noKAgtqtHhcXOMDQ0NOsu40xBXUB9NfQCmOrMNTY2orCwEBwOByaTiQ1SGo2GHcxKgxT9DGfTVdZo\nNGhoaHA4vYfCkt9lbzdQVVWFsbEx5ObmOsya2tvbodFokJ2djZMnT+Kyyy6zOuYCYs0Di50OMR/4\n7LPP8F//9V/44x//6HLtmTNnEBcXN+cibEsc+fRLhEilWJGbCaFgKujQm4wGKcovozcZ/fL0qdrQ\n0ACRSORWl9Fb8OXQC+pm2traCp1Ox7LIbY0C54L60t7ejvHx8Wm2M7Zw5t9FHVMbGhqwYsUKuxkf\n3Z7KZDK0tbUtZLkP4A9cnuH222/Hli1bcO211zpdp9PpUF1d7TNvc2DKSbSyshKxsbEYGxuDVqsF\nwzAQiURWmZQ3z8doNKK8vNynbHNKpMzMzGQdDbwBy86oRqOZpnns7OxEamqqzx5GhBCcPXsW0dHR\nLqkgNHjZ1rtoBtXT04O+vj6HzQ2j0YiysjIYjUZcccUV7M8XGGse8Acuz9DZ2Ykbb7wRJ06ccMla\n96YI2xbUMthSs8flcsHhcFj/JW929pxhPraM1Ebb1VbKERx9fpZbPdvPj44bo46gvsBMZEj2/Lv+\n+c9/shlUXV0dxGKxw8aRRqPBqVOncOWVV7KZmT9wLSG88sorUKvVePzxx52uo5lBVlaW2yJsW1ha\nBtObbGJiwkqUTd0iGIYBIQTV1dVITEyc09FmtmhoaGD5Wr5Ca2srDAbDNCG2LewJsy0tly0/P1fo\n7e1Fb28vli9f7tO6XlNTE1tjcwR79S7LwGUymXD69GlkZmba7UBPTEygqqoKQqGQzcwWGGse8Acu\nz2EwGHD55Zfj4MGD00aj22JkZATNzc1uZSOWlsH0JqNsacsg5UqUPR/bVLplzMvL85l+krpzpqSk\nQCqVskHekm1O6Rs0i5JIJB5ZLluirq4OUql0QQZpy3oXwzA4deoUVq1axb4+NjaGM2fO2B0MOzIy\ngosXL4LD4SA4OBiJiYn+wOVrlJaWYs+ePTCZTPjxj3+Mn//851avt7e3Y/v27VCr1TCZTHjhhRew\nceNGt9//m2++wa9+9St8+OGHLm8ClUqF0NBQKzqG5TBQS+cIqtmjN5mnlIqLFy9Cp9O5vNC9CV9u\nGSnHbGBgABcuXIBIJGI5SDSLmonzxkxAg/Ty5cs9zqRnCppJx8fHu3QFocELmAqytt30np4edHV1\nTft/6u/vx/DwMFJTU3H69Gmkp6dDJpMtlOk+FEs3cJlMJiiVSnz22WeIi4tDcXExDh06ZDVH7yc/\n+Qny8/Nx//33Q6VSYePGjU61j/awfft2bNy4ETfddJPTdZOTkygrK0N8fDzLlbJ0jqA3mTefbIQQ\nVFRUQKlU+mxqMzA3W0aaiVpmUnq9npXE0CG12dnZPtu+uUtX8Cb0ej0qKircmkxkNBoxNjaG9vZ2\n5OXlTXu9vr6eVQdQWM5fnJiYQGVlJVauXOkz00o3sbhF1s5w+vRppKamstu4zZs348iRI1aBi2EY\naDQaAFMpsids7P379+Oaa67B+vXr2Sev0Wi0IiJSjk9gYCD6+vqgUCigUCjm/CnGMAzS09NnVcD2\nBKmpqSgvL4dMJvNoy+hKEhMaGoqEhASrTJR23wYGBhwOjvA2JBIJ5HK5XbPDuUJAQADS0tJQV1eH\n/Px8h/+nk5NTFt/9/f0OH4ZpaWmsnIeK9CnhGJiSPCmVSvT09LgshywULPrAZU9gbWtps3fvXlx7\n7bV47bXXMDY2hs8//3zGx+HxeFi7di22b98OPp+Pf/u3f2M7U1RyIRKJwOFw2HoMwzA+S71FIhFk\nMhna29t9wnkCpj4TpVIJlUrlcsvoiSTGHiyF2CEhIT7rgiUmJqKyshJDQ0M+a4SEhYVBrVajpaUF\nKSkpbE3PtnEjkUgQFhYGiURiV8/I4XCQk5ODyspK1tJZr9dbFe0jIiIWWn3LKRZ94HIHhw4dwo4d\nO/DII4/g5MmT2Lp1K2pra93OTN577z28+eabyM3NRW9vL372s5+xsgl7oBkQ9VL31ZaGSpDkcrnP\nUv6wsDB2+CndMjqSxAQHB7PZiyuLG2egQuz6+nrk5OT45PNlGAZZWVmoqqpyOaHaG6C6UbPZjI6O\nDnR3d7NqB1tJFgWdUm5PzxgUFASFQsFaOtvKfRYbFn3gckdg/eabb6K0tBQAsGrVKrbQ667n0113\n3YW77roLAFBWVobHH38cJSUlTn8nODjYpyJsYOrJmpaWxrpg+uKGNplMrDPt0NAQxsfHAXwviYmO\njp6T7XJERAT6+vp8KsQWCARISUnxesC09eKytK2WSCTIz89HXV0dsrOznTZwqJ6RBiXb4CWXy9nB\nuPYC1wJizLvEog9cxcXFaGpqQktLC2JjY3H48GG8//77VmsSEhJw4sQJ7NixA/X19dDpdB7XR4qL\ni5GSkoI///nP+NGPfuR0rWUG5CvagFQqRXBwsFUG5C1Y1vSopIj6cEVGRkKtVqOwsNBn2+O0tDSf\nC7HlcjkGBwfdNli0hCMKB+2OisViu5kUAKt6l7MAQ/WV9vy7gCmPtfLycnZ022LFou8qAlPWzQ8+\n+CBMJhN27tyJJ598Er/4xS9QVFSEkpISqFQq3Hvvvaws5qWXXnIp43GGoaEhrFu3DsePH3fZxRsY\nGEBXV5dPRdhU0lFQUOAxxcKVJIbq9iyf6vNBTKUZhC+F2NQtwxlFwjJI0UBlGaQ8oXCcP3+e9Uhz\nBmd6RmAqw/v666+xdu1aq+vDFw4nM8TSpUPMF9566y1UVVXhxRdfdLn2zJkziI2N9emknpm4Vngi\nibGH+SCmAlNC7ICAAKvBpnON0dFR1NfXo6ioCAzDsDUpGqiouygNUpSnN5vgajabWRKuKwtvR3pG\nYCqo/v3vf0dQUBB7/gtQ7gP4A5f3YTabsW7dOuzfv9+la+Z8sNsBoKamBpGRkVa1PDqM1huSGHuY\nDy2j2WxGWVkZsrKyvCrEtgdLf/iOjg521qOll5k3gpQjUA2lOw0Co9EIQsi0ehcdYyaTyUAIgUKh\nWIisecAfuOYGZ86cwe7du/HJJ5+4zEba29thMBh8Nk2GEAKtVovq6mpERUVBq9XOiSTGHurr6yGR\nSHy6ZbTMgLzFY7MMUrayLPoZtrW1ITk52efZdEdHB/Ly8lxSUOz5d42Pj+PcuXPIy8tDeXk5kpKS\n3J5e7mP4A9dc4cEHH0R6ejq2bdvmdJ03RNiO4GhKDH3qE0KQkZExJ5IYe5ivLWNrayuMRqNd+2JX\nsA1Slq6wtjUpS0xOTqKystInFAlLnDt3DgKBwOX22F69S61Wo7OzE1lZWewIs+Li4oVo2ewPXHMF\njUaDH/zgBzh27JhLYuLIyAg7gNTTAOJIEmN7g1kGraqqKiQnJ3t9tJkzzMeWkUqfUlNTnY4asxW4\n2/sMZ6Id7e/vR2dnp8+GewDfPwjT0tJczh+wrXf19fVhZGSEVQEMDQ1hcHAQ2dnZvjj1mcAfuOYS\n77//Pr788ku8+uqrLtfW19dDKpW65YnvTBJjWTh3dYNNTEywLgG+rLHNx5bR9m91pn203DLPtqPW\n0NCA4OBgn8zapBgfH8fZs2fdGqtmWe/q7OyE2Wy2yta4XO5CE1gDl3rgcuUeAQB/+tOfsHfvXnZU\nky0XzBkIIbjmmmvwzDPPoLCw0Olag8GA8vLyaQZ1riQxlIbg6Xakra0NBoPBo22Up/D1lpEGqdbW\nVoyMjLDSFkvGuTuB3hOYTCa2FDDXDQJL9Pb2oqenxyUh1rLedfHiRQQHByMqKop9ncfj+fSh5iYu\n3cDljntEU1MTNm3ahC+++AKhoaEeTVKur6/Hzp07cfz4cZcXQGdnJwYGBiCTyexKYmbrDW8PhBCU\nlZUhIyPDp7WMudoyWk4qots9y2x0YGAA8fHxPh1wqtVqWWsZX2e2IpHIZbZHt4wtLS2IioqyKm3Y\nmxy0ALB03SFcwR33iN///vfYtWsXWwPyZPx7RkYG1q5di7feegv33nsv+3PLKTE0SAFTbW2BQDBn\nkhhbUHEytWfxVS0mLCwMvb29s2Ly0yBlud2zdJEICwtDUlKSVTYaGxuLyspKyGQynxXNRSIRoqOj\ncf78+WlTo+cSSqWSnf7t7KFkNBoxMjKC4eHhaaz/xST3AS6BwOWOe0RjYyMAYPXq1TCZTNi7dy+u\nv/76GR/r4Ycfxpo1a3Dx4kXExcUhNzcXANgMynI0/djYGOrq6lgOjS8gFosRGhrqU/0k8L3MxB37\nG8sgZc/qRiaTTQtS9hAYGIiUlBR2OrWvbsz4+HhUV1djYGDAZxQJLpeLrKws1NTUoKioCDweDwaD\nwapDOj4+zlJhEhMTwefzYTabF2KW5RaWfOByB0ajEU1NTfjqq6/Q0dGBNWvWoKamxmlnyhZXX301\nxsfHkZSUhHPnzuHWW2/F8uXLHV4YVITd0dHh0yBC9ZMRERE+0/dR+5v6+nor7pGrIBUeHo7k5GSP\nMya5XI7+/n709PT4bMtIXSQqKirmrJ5mC4PBgMnJSQiFQpw8eRI8Hg98Pp+t60VGRlrx9Wi9y5Ge\ncTFgyQcud9wj4uLicNlll4HP5yM5ORlKpRJNTU0oLi52+zjHjx9nvbhuvPFGVtvnDPMhwuZyuVAq\nlWhoaHBJYPQmQkND0dHRAZVKBR6PB41GwzYfxGKx235cM4WlENtXn3FAQAAUCgVUKpXXP2Oj0Tgt\nk+JyuZBIJIiIiAAhBDKZzKkAnF6XRqMRJpMJPB5v0W0Vl3xx3mg0QqlU4sSJE4iNjUVxcTHef/99\nZGVlsWtKS0tx6NAhvPPOOxgYGEB+fj6qq6s9nqnX3NyMLVu24PPPP3dZu5oPETZg3xvfW6A0Dsua\nFJ2erVaroVQqER4e7jOm9nwIsYEpkmhQUJDHGTUdUEuD1NjYmJWGVCKRIDg42Opvmkl3kxbrBQKB\nT4nCM8ClW5zn8Xg4cOAArrvuOtY9Iisry8o94rrrrsPx48eRmZkJLpeL/fv3z2oQaEpKCjZu3Ij/\n+Z//we7du52uDQ8PR1dXl09rIoB13Wk22wXLIGWPxhEREWGVSQ0NDaGtrc2qFT/XCA0NhVgsnrfa\nHj2+M5hMJqsgRS2DaIBKTk6GUCh0mcXTepc73U2GYTA8PIzq6mqUlJQsRDqEQyz5jGu+oNPpsGrV\nKnz44Ycuje7mS4Td19eHvr4+txnTltOK7AUpdweBzAcxdb54VvYoEo6ClG0mNZvCeUdHB3p6etip\nP4QQqNVqVFVVobKyElVVVTh//jxCQ0NRWFiI5557bqENygAuZR7XfOKTTz7BW2+9hXfeecfl2vb2\nduj1ep8SRAHHljuOCLHemFY0X1rGuRBiu4LJZEJzczNGRkYgFApZTziRSMQGKVtfs9mCEAKNRoN7\n770XYWFhGB8fR2NjI8RiMQoKClBUVITi4mKkpaUt9CzLH7jmC7fccgvuueceXHnllU7XzaUI2xkm\nJydRUVGBzMxMq0BlS4iVSCRe5ZrRLaMvGwQA0NLSArPZPCcuHZZe+zTYA1P8Lo1Gg5iYGMTHx3s9\nSNHBrzSTamhogFAoxPLly1FaWooDBw5gw4YNC1HS4wr+wDVfuHjxIm6++WacOHHCZWvcGyJsV6CZ\nlK1VC4fDQVxcHBuofHGRz8eWkQqxFQqFS3GyM9gGKa1WC0LItEyKZjR0TuJsXGmpg8XZs2fZIKVS\nqcDn85GXl4fCwkIUFxcjKyuLrVtWVlbi+PHjdmVuiwD+wDWfePHFF6HT6fDv//7vLtfORITtCvaC\nlNlsZgdZ0CDF5XJRWVmJ1NTUWd3MM8V8bRmpONndmqKzz9GytufqvQYHB9HW1ua2/GlychK1tbVs\nkKqtrQXDMMjJyWGDVE5OzkK0XPYW/IFrPqHX63H55ZfjD3/4g0v/JEcibFdwNBKM3lyUve8okxob\nG0NtbS2Ki4t9yqaery1jR0cHtFot0tPTrX5u2yWl2+aZBilHaGxsREBAwLTZl3q9HvX19WyQOnPm\nDMxmM7KystiaVG5u7pwYPy5gXJqBq7q6Gvfffz80Gg24XC6efPJJ3H777XbXuuMgAQAfffQRbrvt\nNpSVlbEdG3fw5Zdf4je/+Q0OHz7scm13dzfUajUyMjLsvm5vm+KNm6ulpQWEEJ9PM56vLWNVVRXk\ncjk4HI4V34zW9uZi22w2m7Fr1y6sXbsWer0e1dXVqK6uxuTkJDIzM1FYWIiioiIUFBRM42hdgrg0\nA1djYyMYhoFCoUBXVxcKCwvZrZgl3HGQAKa6UjfccAP0ej0OHDgwo8AFAHfccQduvfVWbNiwwek6\nOgk7NTUVYrF4ToKUPcxXg4BOJcrPz5+zLaOtvEij0cBoNEKn0yEhIYHlWnmbFGsymdDY2MhmUtXV\n1Wyn8cEHH8Tq1atRUFAAiURyqQcpe1haBNSysjLcc889OH36NEwmE1asWIEPPvhgGh9JqVSy/46J\niWG1a7aByx0HCQB4+umn8dhjj2H//v0enfcrr7yCDRs2YN26dXZ1gmazmd3m8fl8VFRUsJIYOnPP\nW0HKHiwHyhYWFvrsRuLxeOxxvbFltDQPtAxSlhpISort7e1Fb2+vV7qMZrMZzc3NbJCqqqpinUYL\nCwvxwx/+EPv27UNoaCjeeustXLhwwWW32Q/7WJSBq7i4GCUlJXjqqacwMTGBu+66yyWJ8vTp09Dr\n9XYvUHccJCorK3Hx4kXccMMNHgeu6Oho3H333Xj55Zfx8MMPs6JiS7sbmkklJCQgKCjI52O3QkJC\nIJFIPBp4OhtQ+5uZTqa2Z8NsMBhY80BXbhKRkZGsEHsmbH6z2Yy2tjYrQufg4CCWLVuGgoICbNiw\nAU8//TTCw8PtBuKdO3diYmLC7eP5YY1FGbgA4Be/+AWKi4shEAjwn//5n07Xdnd3Y+vWrXjnnXc8\nKjybzWY8/PDDePvttz0826kBBYcOHUJ1dTWOHDmC//u//8Njjz2G1atXW9ndWEIsFqOsrAyRkZE+\n7bqlpKSgrKwM4eHhPj0ulciEhYXZPa6jgRY0SIWGhiIxMXHGEqa0tDTWz8recc1mM7q6ulBRUYHK\nykpUV1ejp6cHiYmJKCwsxPr16/Hoo48iMjLS7WyRYZiFyFRfNFi0gWtwcBBarRYGgwE6nc5hTUaj\n0eCGG27Ac889h5UrV9pd48pBYnR0FLW1tVi3bh0AoKenByUlJTh69KjbdS6z2QyGYfDAAw9g+/bt\neP7553H77bc7vdC5XC5SU1Nx7tw51tvLF+ByuVAoFGhoaPDpAAhL+5vc3FyrGZB0chEdaCGVSpGQ\nkOAVKgCfz4dCocDLL7+Mn//85+jv70dlZSWbSXV0dCAuLg6FhYVYvXo19uzZg9jYWH9Nah6xaIvz\nJSUl2Lx5M1paWtDd3Y0DBw5MW6PX67FhwwbcdNNNePDBBx2+lzsOEpZYt24dXn755RkX5y1xzz33\nYP369fjhD3/ocu3Zs2cRExPjUxE2ANTV1SE8PByRkZFzfizL8WpdXV0ghFhp9+yNBpstCCFskKqq\nqsLx48cxMDCA5ORktrtXXFyMhISERWu4t8ixtIrz7777Lvh8Pu644w6YTCZcfvnl+OKLL7B+/Xqr\ndX/605/w9ddfY3BwkN3mvf3228jLy7Na546DhLfx4osv4qqrrsI111zjUvSrVCpRXV2N0NBQn2rL\nFAoFKioqEBYW5tVu2+TkpFUmpdPprMbV5+fno6amhp0F6Q0QQjA0NGRVk7pw4QJkMhkbpG6//Xbc\nfffdeP31131qvezHzLFoM66lgN/97nc4d+4c9u3b53LtfImwe3p6MDg46DD7dAXb7d7ExAQbpGhG\nZW9Q7eDgINrb2z3qMhJCMDIygurqajZINTU1QSKRWGVSCoVi2oOgrq4OIpHIpw0RPxzi0uRxLXSY\nzWasWbMGv/3tb6dRL2xBp/T4mmNFCEF1dTUSExNdDrzV6/VW3T1Ln3P6NZNp2vX19QgJCXHaZSSE\nQKvVWgWpc+fOITg4mHVCKCoqQnp6+mIUGV/qWNqBq6amBlu3brX6WWBg4DRKw0JEZWUlHnnkEXz8\n8ccub2hfiLDtwZ5fmL1hDJY+5xKJZNbyFKPRiG+//RZKpRJyuZwlkVqKjOvr6xEYGIi8vDw2k8rM\nzPSZu6ofc4qlHbgWO3bt2oWCggJs2bLF5dqGhgZIJJIZcZ1mC4PBgObmZmi1WgQEBGB8fBw8Hs+q\neC4UCr0+N3FychLvvfceDh48iOzsbNTV1YHH4yE3N5cVGS9fvnzRDnyYS+zcuRMff/wx5HI5amtr\np71OCMGePXtw7NgxCIVCvP322ygoKJiHM3WKpVWcX2p47rnnsGbNGmzYsMHlZKGUlBSUl5cjIiJi\nTrIKZz7nk5OTiI+Ph1wu93rGNzk5CZVKxWZSNTU1IIQgOzsbwcHBSE5Oxu9///uF6o2+4LBjxw7s\n3r0b27Zts/v6J598gqamJjQ1NeHUqVO4//77F8UOhcIfuBYApFIpfvazn+E//uM/8Morrzhdy+fz\nkZSUhKamJpd1MVcwmUxWVi1ardZqGENycrKV0Fer1UKlUiEiImJWgctgMFg5IZw9exYGgwFZWVko\nLCzEzp07kZ+fz2Zxo6OjeOihh5ayfYvXsWbNGrS2tjp8/ciRI9i2bRsYhsHKlSuhVqvR3d3t08nf\ns4E/cC0QbN26FQcPHkRVVRXy8/Odro2KikJXVxfUarXbsx9d+ZwnJia69DkXiUSQyWRob2+fZsvi\nCEajEY2NjaioqGBFxjqdDhkZGSgsLMSdd96JV155BWKx2GEwFIvFeOONN9w6nh/uwZ7MrbOz0x+4\nlhpc2d78+te/xhtvvAEej4eIiAi89dZbM2qpMwyD1157DT/96U/x6aefOg0gDMMgPT2dHcJgu9Zk\nMk1zlGAYZkZByhEsZ0HaSlZMJhPOnz9vJTIeHR1FWloaCgsL8aMf/QgvvPACQkJC/KxzP2YFf+By\nAyaTCbt27bKyvSkpKbHaquXn56O8vBxCoRD//d//jUcffRQffPDBjI6TnZ2NlStX4t1338WOHTuc\nrg0ODoZMJkNbWxvCwsKmBSkq1o6Pj/fqMAYOh4Pk5GQ88MADePLJJ628zoeHh5GamorCwkKUlJTg\n2WefRVhYmD9I2cDVQ7C9vR3bt2+HWq2GyWTCCy+8gI0bN3r1HNwZlLyQ4Q9cbsAd2xtLe5KVK1fi\nvffe8+hYe/fuxerVq3HjjTfanbxjO4xBq9VidHQUoaGhiIuLsyvWni3MZjM6OjrY7R7V79199924\n+eabce211+KJJ56Yde3rUoA7D8F9+/Zh06ZNuP/++6FSqbBx40an9SpPUFJSggMHDmDz5s04deoU\nQkJCFs02EfAHLrfgju2NJd58802XZoGOIBaL8dRTT+GZZ57BXXfdheDgYPD5fIyOjloNY6COEmq1\nGh0dHVbnNxsQQtDd3W3lhNDV1YX4+HgUFhZizZo1ePjhhyESiXDFFVfggQceQEREhFeOfSnAnYcg\nwzDQaDQAprh7nlBftmzZgq+++goDAwOIi4vDs88+C4PBAAC47777sHHjRhw7dgypqakQCoU4ePCg\nF/4638EfuLyM9957D+Xl5fjb3/4249/t7OzEr371K1RVVaGxsRHt7e3Ys2cPioqKoFQq7WZSMpkM\nnZ2d6O/vn3EAIYSgt7eX3epRz7GYmBgUFhbisssuw65duxAXF2d3q3n06NFZTfy+FOHOQ3Dv3r24\n9tpr8dprr2FsbAyff/75jI9z6NAhp68zDIPXX399xu+7UOAPXG7A3XrA559/jueeew5/+9vfPGrd\ni8VibN68GS+++CK6urqwdetWrFu3zqVUhYqww8LCHG4TCSEYGBhgt3qVlZVoaWmBXC5n9Xv33HMP\nkpKS3K6H+fV8c4NDhw5hx44deOSRR3Dy5Els3boVtbW1focKC/gDlxsoLi5GU1MTWlpaEBsbi8OH\nD+P999+3WlNVVYWf/vSnKC0thVwu9+g4EokEV1xxBYCpYHTNNdfgjTfewH333ef09wQCAWJiYvDl\nl1/i6quvdjluvaioCHfeeSdSU1P9N4OP4c5D8M0330RpaSkAYNWqVdDpdBgYGPD4ulqSIIQ4+/Lj\nO/z1r38lCoWCLFu2jOzbt48QQsjTTz9Njhw5Qggh5KqrriJyuZzk5uaS3NxcctNNN836mGNjYyQ3\nN5c0NzeTsbExu19arZZ0dXWRjz/+mCgUCrJx40aSk5NDVq9eTf71X/+VvPPOO0SlUhGj0Tjr81nq\n+OSTT4hSqSQpKSnk+eeft7vmgw8+IBkZGSQzM5Ns2bJlxscwGAwkOTmZXLhwgUxOTpKcnBxSW1tr\nteb6668nBw8eJIQQolKpSHR0NDGbzTM+1hKAw9jk1youcBw9ehSHDx/GG2+84XTcel5eHsLDw/Hp\np5/im2++8YuMZwh3Jj01NTVh06ZN+OKLLxAaGoq+vj6PsqBjx47hwQcfZL3fnnzySSvvN5VKhXvv\nvZeltrz00ku49tprvfnnLhb4RdaLGWvWrEFgYCAGBwenjVvPzs62ClIvvPACtm3b5lMR9lLAyZMn\nsXfvXnxvMEOrAAAErUlEQVT66acAgOeffx4A8Pjjj7NrHn30USiVSvz4xz+el3O8BOEXWS9mvPzy\ny+jr68M111zjsujvaJCtH87hTrevsbERALB69WqYTCbs3bsX119/vU/P048p+APXIsCKFSvm+xT8\nwJTusqmpCV999RU6OjqwZs0a1NTUuK0X9cN78LeU/FjQKC0tRVpaGlJTU/HCCy84XPfRRx+BYRiU\nl5d7dBx3un1xcXEoKSkBn89HcnIylEolmpqaPDqeH7ODP3D5sWBB5TGffPIJVCoVDh06BJVKNW3d\n6OgoXn31VVx22WUeH8uS8qLX63H48OFpQ1L+5V/+BV999RUAYGBgAI2NjSwD3g/fwh+4/FiwsJTH\nBAQEsPIYWzz99NN47LHHZmUyaDnpKSMjA5s2bWInPR09ehQAcN1110EmkyEzMxNXXnkl9u/f71cO\nzBeccSV8T9tY3HDFA9LpdGTTpk0kJSWFrFixgrS0tPj+JBcRPvzwQ3LPPfew37/77rtk165dVmsq\nKirILbfcQgghZO3ataSsrMyn5+jHnMJhbPJnXF6CO9uaN998E6GhoTh//jweeughPPbYY/N0tksD\nZrMZDz/8sEvXWD+WHvyBy0twZ1tz5MgRbN++HQBw22234cSJEyDOeXSXNFwVzEdHR1FbW4t169Yh\nKSkJ3377LUpKSjwu0PuxeOAPXF6CIytcR2t4PB5CQkIwODjo0/P0Blx1+n79618jMzMTOTk5uOqq\nq9DW1ubRcVwVzENCQjAwMIDW1la0trZi5cqVOHr0KIqKijz+2/xYHPAHLj9mBHe2xNQN9uzZs7jt\nttvw6KOPenQsdwrmflya8BNQvQR3eEB0TVxcHIxGI0ZGRhZdV8qXbrAAsHHjxmm2xb/85S/trqVU\nBT+WPvwZl5fgDg+opKQE77zzDgDgz3/+M9avX7/orI7d2RJbYjZusH744QiuRNZ+zAAMw2wE8FsA\nXABvEUKeYxjmlwDKCSFHGYYRAPgDgHwAQwA2E0IuzN8ZzxwMw9wG4HpCyI+/+34rgMsIIbvtrL0L\nwG4Aawkhk749U8/AMEwpgJUAviGE3Djf5+OHffi3il4EIeQYgGM2P/uFxb91AH40V8dnGOZ6AK9i\nKnC+QQh5web1QADvAigEMAjgdkJI6wwP0wnA0uA+7ruf2Z7L1QCexCIKWt9hPwAhgJ/O94n44Rj+\nreISAcMwXACvA9gAIBPAFoZhbEdd3wNgmBCSCuA3AF704FBlABQMwyQzDBMAYDMAq0o5wzD5AH4H\noIQQ0ufBMbwKhmGKGYY5yzCMgGGYYIZh6hiGyba3lhByAsCoj0/RjxnCH7iWDlYAOE8IuUAI0QM4\nDOBmmzU3A3jnu3//GcBVzAyLbIQQI6a2f58CqAfwJ0JIHcMwv2QYhhb19gMQAfiQYZhqhmHmtQVI\nCCnDVHDdB+AlAO8RQmrn85z8mB38W8Wlg1gAFy2+7wBgqzpm1xBCjAzDjACQARiYyYHc2BJfPZP3\n8xF+ialsUQfg3+b5XPyYJfwZlx+XCmSYygLFADxXY/uxIOAPXEsH7hTN2TUMw/AAhGCqSH8p4HcA\nngbwR3hW2/NjAeH/A6QkSGHv5lFVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IAtj6VWZN971",
        "colab_type": "text"
      },
      "source": [
        "## Linear regression with tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyLyphgxN971",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dtype = torch.FloatTensor\n",
        "# dtype = torch.cuda.FloatTensor # Uncomment this to run on GPU"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHsdZ8fHN973",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_t = torch.from_numpy(x).type(dtype)\n",
        "y_t = torch.from_numpy(y).type(dtype).unsqueeze(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ldmIFDbIN975",
        "colab_type": "text"
      },
      "source": [
        "This is an implementation of **(Batch) Gradient Descent** with tensors.\n",
        "\n",
        "Note that in the main loop, the functions loss_t and gradient_t are always called with the same inputs: they can easily be incorporated into the loop (we'll do that below)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuLkxOS_N975",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "414942a8-28ab-4f14-bc36-ae5c0aca68a9"
      },
      "source": [
        "w_init_t = torch.from_numpy(w_init).type(dtype)\n",
        "b_init_t = torch.from_numpy(b_init).type(dtype)\n",
        "\n",
        "w_t = w_init_t.clone()\n",
        "w_t.unsqueeze_(1)\n",
        "b_t = b_init_t.clone()\n",
        "b_t.unsqueeze_(1)\n",
        "print(\"initial values of the parameters:\", w_t, b_t )\n",
        "\n",
        "# our model forward pass\n",
        "def forward_t(x):\n",
        "    return x.mm(w_t)+b_t\n",
        "\n",
        "# Loss function\n",
        "def loss_t(x, y):\n",
        "    y_pred = forward_t(x)\n",
        "    return (y_pred - y).pow(2).sum()\n",
        "\n",
        "# compute gradient\n",
        "def gradient_t(x, y):  # d_loss/d_w, d_loss/d_c\n",
        "    return 2*torch.mm(torch.t(x),x.mm(w_t)+b_t - y), 2 * (x.mm(w_t)+b_t - y).sum()\n",
        "\n",
        "learning_rate = 1e-2\n",
        "for epoch in range(10):\n",
        "    l_t = loss_t(x_t,y_t)\n",
        "    grad_w, grad_b = gradient_t(x_t,y_t)\n",
        "    w_t = w_t-learning_rate*grad_w\n",
        "    b_t = b_t-learning_rate*grad_b\n",
        "    print(\"progress:\", \"epoch:\", epoch, \"loss\",l_t)\n",
        "\n",
        "# After training\n",
        "print(\"estimation of the parameters:\", w_t, b_t )"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "initial values of the parameters: tensor([[0.5220],\n",
            "        [0.3657]]) tensor([[0.2867]])\n",
            "progress: epoch: 0 loss tensor(25.8181)\n",
            "progress: epoch: 1 loss tensor(23.1608)\n",
            "progress: epoch: 2 loss tensor(21.5332)\n",
            "progress: epoch: 3 loss tensor(20.0250)\n",
            "progress: epoch: 4 loss tensor(18.6228)\n",
            "progress: epoch: 5 loss tensor(17.3190)\n",
            "progress: epoch: 6 loss tensor(16.1067)\n",
            "progress: epoch: 7 loss tensor(14.9796)\n",
            "progress: epoch: 8 loss tensor(13.9315)\n",
            "progress: epoch: 9 loss tensor(12.9569)\n",
            "estimation of the parameters: tensor([[ 0.8858],\n",
            "        [-0.7084]]) tensor([[0.4541]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FS_7oCWgN977",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "9384e4d2-2f11-48c3-b3f2-b39b1c58d770"
      },
      "source": [
        "plot_views(x, y, w_t.numpy(), b_t.numpy())"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8gmAwCIZhEI7E8OJPDuE3770NoijCbDYjGAxieHgY+/bty/ucT81E8UffP4Ws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\nngbwR3hW2/NjAeH/A6QkSGHv5lFVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJXOpOPpN978",
        "colab_type": "text"
      },
      "source": [
        "## Linear regression with Autograd"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQi-vPQaN978",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5c55950d-fa5b-44ae-f67d-a7ad5997ac5e"
      },
      "source": [
        "# Setting requires_grad=True indicates that we want to compute gradients with\n",
        "# respect to these Tensors during the backward pass.\n",
        "w_v = w_init_t.clone().unsqueeze(1)\n",
        "w_v.requires_grad_(True)\n",
        "b_v = b_init_t.clone().unsqueeze(1)\n",
        "b_v.requires_grad_(True)\n",
        "print(\"initial values of the parameters:\", w_v.data, b_v.data )"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "initial values of the parameters: tensor([[0.5220],\n",
            "        [0.3657]]) tensor([[0.2867]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEGOC9uIN979",
        "colab_type": "text"
      },
      "source": [
        "An implementation of **(Batch) Gradient Descent** without computing explicitly the gradient and using autograd instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkEqcNoxN97-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "outputId": "87d67bcc-6cec-4737-ddd1-d7413092d483"
      },
      "source": [
        "for epoch in range(10):\n",
        "    y_pred = x_t.mm(w_v)+b_v\n",
        "    loss = (y_pred - y_t).pow(2).sum()\n",
        "    \n",
        "    # Use autograd to compute the backward pass. This call will compute the\n",
        "    # gradient of loss with respect to all Variables with requires_grad=True.\n",
        "    # After this call w.grad and b.grad will be Variables holding the gradient\n",
        "    # of the loss with respect to w and b respectively.\n",
        "    loss.backward()\n",
        "    \n",
        "    # Update weights using gradient descent. For this step we just want to mutate\n",
        "    # the values of w_v and b_v in-place; we don't want to build up a computational\n",
        "    # graph for the update steps, so we use the torch.no_grad() context manager\n",
        "    # to prevent PyTorch from building a computational graph for the updates\n",
        "    with torch.no_grad():\n",
        "        w_v -= learning_rate * w_v.grad\n",
        "        b_v -= learning_rate * b_v.grad\n",
        "    \n",
        "    # Manually zero the gradients after updating weights\n",
        "    # otherwise gradients will be acumulated after each .backward()\n",
        "    w_v.grad.zero_()\n",
        "    b_v.grad.zero_()\n",
        "    \n",
        "    print(\"progress:\", \"epoch:\", epoch, \"loss\",loss.data.item())\n",
        "\n",
        "# After training\n",
        "print(\"estimation of the parameters:\", w_v.data, b_v.data.t() )"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "progress: epoch: 0 loss 25.81812858581543\n",
            "progress: epoch: 1 loss 23.16082191467285\n",
            "progress: epoch: 2 loss 21.533184051513672\n",
            "progress: epoch: 3 loss 20.024999618530273\n",
            "progress: epoch: 4 loss 18.622756958007812\n",
            "progress: epoch: 5 loss 17.318979263305664\n",
            "progress: epoch: 6 loss 16.106725692749023\n",
            "progress: epoch: 7 loss 14.979554176330566\n",
            "progress: epoch: 8 loss 13.931479454040527\n",
            "progress: epoch: 9 loss 12.956937789916992\n",
            "estimation of the parameters: tensor([[ 0.8858],\n",
            "        [-0.7084]]) tensor([[0.4541]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60tVqdcFN97_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "ed2cab2e-4868-4ea8-be4b-d18cc5e35013"
      },
      "source": [
        "plot_views(x, y, w_v.data.numpy(), b_v.data.numpy())"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8gmAwCIZhEI7E8OJPDuE3770NoijCbDYjGAxieHgY+/bty/ucT81E8UffP4Ws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\nngbwR3hW2/NjAeH/A6QkSGHv5lFVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ivn4ZPjzN98D",
        "colab_type": "text"
      },
      "source": [
        "## Linear regression with neural network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10Cs5TsSN98D",
        "colab_type": "text"
      },
      "source": [
        "An implementation of **(Batch) Gradient Descent** using the nn package. Here we have a super simple model with only one layer and no activation function!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wJhXRUVN98D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "3c0076ca-6a18-4ff2-9954-24a947a4919e"
      },
      "source": [
        "# Use the nn package to define our model as a sequence of layers. nn.Sequential\n",
        "# is a Module which contains other Modules, and applies them in sequence to\n",
        "# produce its output. Each Linear Module computes output from input using a\n",
        "# linear function, and holds internal Variables for its weight and bias.\n",
        "model = torch.nn.Sequential(\n",
        "    torch.nn.Linear(2, 1),\n",
        ")\n",
        "\n",
        "for m in model.children():\n",
        "    m.weight.data = w_init_t.clone().unsqueeze(0)\n",
        "    m.bias.data = b_init_t.clone()\n",
        "\n",
        "# The nn package also contains definitions of popular loss functions; in this\n",
        "# case we will use Mean Squared Error (MSE) as our loss function.\n",
        "loss_fn = torch.nn.MSELoss(size_average=False)\n",
        "\n",
        "# switch to train mode\n",
        "model.train()\n",
        "\n",
        "for epoch in range(10):\n",
        "    # Forward pass: compute predicted y by passing x to the model. Module objects\n",
        "    # override the __call__ operator so you can call them like functions. When\n",
        "    # doing so you pass a Variable of input data to the Module and it produces\n",
        "    # a Variable of output data.\n",
        "    y_pred = model(x_t)\n",
        "  \n",
        "    # Note this operation is equivalent to: pred = model.forward(x_v)\n",
        "\n",
        "    # Compute and print loss. We pass Variables containing the predicted and true\n",
        "    # values of y, and the loss function returns a Variable containing the\n",
        "    # loss.\n",
        "    loss = loss_fn(y_pred, y_t)\n",
        "\n",
        "    # Zero the gradients before running the backward pass.\n",
        "    model.zero_grad()\n",
        "\n",
        "    # Backward pass: compute gradient of the loss with respect to all the learnable\n",
        "    # parameters of the model. Internally, the parameters of each Module are stored\n",
        "    # in Variables with requires_grad=True, so this call will compute gradients for\n",
        "    # all learnable parameters in the model.\n",
        "    loss.backward()\n",
        "\n",
        "    # Update the weights using gradient descent. Each parameter is a Tensor, so\n",
        "    # we can access its data and gradients like we did before.\n",
        "    with torch.no_grad():\n",
        "        for param in model.parameters():\n",
        "            param.data -= learning_rate * param.grad\n",
        "        \n",
        "    print(\"progress:\", \"epoch:\", epoch, \"loss\",loss.data.item())\n",
        "\n",
        "# After training\n",
        "print(\"estimation of the parameters:\")\n",
        "for param in model.parameters():\n",
        "    print(param)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "progress: epoch: 0 loss 25.81812858581543\n",
            "progress: epoch: 1 loss 23.160825729370117\n",
            "progress: epoch: 2 loss 21.533184051513672\n",
            "progress: epoch: 3 loss 20.02499771118164\n",
            "progress: epoch: 4 loss 18.622758865356445\n",
            "progress: epoch: 5 loss 17.318979263305664\n",
            "progress: epoch: 6 loss 16.10672378540039\n",
            "progress: epoch: 7 loss 14.979554176330566\n",
            "progress: epoch: 8 loss 13.931480407714844\n",
            "progress: epoch: 9 loss 12.956938743591309\n",
            "estimation of the parameters:\n",
            "Parameter containing:\n",
            "tensor([[ 0.8858, -0.7084]], requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([0.4541], requires_grad=True)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9uNbWnYN98E",
        "colab_type": "text"
      },
      "source": [
        "Last step, we use directly the optim package to update the weights and bias."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgCI_pMlN98F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "0e576304-82c6-428c-b70c-b74d4a3097fc"
      },
      "source": [
        "model = torch.nn.Sequential(\n",
        "    torch.nn.Linear(2, 1),\n",
        ")\n",
        "\n",
        "for m in model.children():\n",
        "    m.weight.data = w_init_t.clone().unsqueeze(0)\n",
        "    m.bias.data = b_init_t.clone()\n",
        "\n",
        "loss_fn = torch.nn.MSELoss(size_average=False)\n",
        "\n",
        "model.train()\n",
        "\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "\n",
        "for epoch in range(10):\n",
        "    y_pred = model(x_t)\n",
        "    loss = loss_fn(y_pred, y_t)\n",
        "    print(\"progress:\", \"epoch:\", epoch, \"loss\",loss.item())\n",
        "    # Zero gradients, perform a backward pass, and update the weights.\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    \n",
        "# After training\n",
        "print(\"estimation of the parameters:\")\n",
        "for param in model.parameters():\n",
        "    print(param)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "progress: epoch: 0 loss 25.81812858581543\n",
            "progress: epoch: 1 loss 23.160825729370117\n",
            "progress: epoch: 2 loss 21.533184051513672\n",
            "progress: epoch: 3 loss 20.02499771118164\n",
            "progress: epoch: 4 loss 18.622758865356445\n",
            "progress: epoch: 5 loss 17.31897735595703\n",
            "progress: epoch: 6 loss 16.10672378540039\n",
            "progress: epoch: 7 loss 14.979554176330566\n",
            "progress: epoch: 8 loss 13.931480407714844\n",
            "progress: epoch: 9 loss 12.956938743591309\n",
            "estimation of the parameters:\n",
            "Parameter containing:\n",
            "tensor([[ 0.8858, -0.7084]], requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([0.4541], requires_grad=True)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogKjBGLwN98G",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 1: Play with the code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4bsECERN98G",
        "colab_type": "text"
      },
      "source": [
        "Change the number of samples from 30 to 300. What happens? How to correct it?\n",
        "\n",
        "In the initialization phase, remove the .clone() What happens? Why?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xxb9lycDN98H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bjr1PVRKN98I",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 2: Logistic regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyrqNvpUN98J",
        "colab_type": "text"
      },
      "source": [
        "Sigmoid function:\n",
        "$$\n",
        "\\sigma(y) = \\frac{1}{1+e^{-y}}\n",
        "$$\n",
        "\n",
        "The model is now\n",
        "$$\n",
        "Z_t = Ber(\\sigma(y_t)), \\quad t\\in\\{1,\\dots,30\\},\n",
        "$$\n",
        "and the task is still to recover the weights $w^1=2, w^2=-3$ and the bias $b = 1$ but now from the observations $(x_t,Z_t)_{t\\in \\{1,\\dots,30\\}}$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dO90cPyJN98J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "04b29c22-ecf1-4106-b3b8-cde6497b9149"
      },
      "source": [
        "from scipy.special import expit\n",
        "xaxis = np.arange(-10.0, 10.0, 0.1)\n",
        "plt.plot(xaxis,[expit(x) for x in xaxis]);"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dhzjUEVGXjOQ8hbtIktq6JkqKCrj6PA2nJ7lN4S4ScHdWbWzmqpkVDCtNaQRKkaylcBcJ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\ni8QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoWfOiYdN98K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from scipy.stats import bernoulli\n",
        "Z = bernoulli.rvs(expit(y))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBJmvh0NN98M",
        "colab_type": "text"
      },
      "source": [
        "What is an appropriate loss function now?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-q0n_SwN98N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3F_K1DbJN98P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# solution!\n",
        "b = b.type('torch.FloatTensor')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}